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feat: Major UI/UX improvements and production readiness
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## Features Added

### Document Reference System
- Implemented numbered document references (@1, @2, etc.) with autocomplete dropdown
- Added fuzzy filename matching for @filename references
- Document filtering now prioritizes numeric refs > filename refs > all documents
- Autocomplete dropdown appears when typing @ with keyboard navigation (Up/Down, Enter/Tab, Escape)
- Document numbers displayed in UI for easy reference

### Conversation Management
- Added conversation rename functionality with inline editing
- Implemented conversation search (by title and content)
- Search box always visible, even when no conversations exist
- Export reports now replace @N references with actual filenames

### UI/UX Improvements
- Removed debug toggle button
- Improved text contrast in dark mode (better visibility)
- Made input textarea expand to full available width
- Fixed file text color for better readability
- Enhanced document display with numbered badges

### Configuration & Timeouts
- Made HTTP client timeouts configurable (connect, write, pool)
- Added .env.example with all configuration options
- Updated timeout documentation

### Developer Experience
- Added `make test-setup` target for automated test conversation creation
- Test setup script supports TEST_MESSAGE and TEST_DOCS env vars
- Improved Makefile with dev and test-setup targets

### Documentation
- Updated ARCHITECTURE.md with all new features
- Created comprehensive deployment documentation
- Added GPU VM setup guides
- Removed unnecessary markdown files (CLAUDE.md, CONTRIBUTING.md, header.jpg)
- Organized documentation in docs/ directory

### GPU VM / Ollama (Stability + GPU Offload)
- Updated GPU VM docs to reflect the working systemd environment for remote Ollama
- Standardized remote Ollama port to 11434 (and added /v1/models verification)
- Documented required env for GPU offload on this VM:
  - `OLLAMA_MODELS=/mnt/data/ollama`, `HOME=/mnt/data/ollama/home`
  - `OLLAMA_LLM_LIBRARY=cuda_v12` (not `cuda`)
  - `LD_LIBRARY_PATH=/usr/local/lib/ollama:/usr/local/lib/ollama/cuda_v12`

## Technical Changes

### Backend
- Enhanced `docs_context.py` with reference parsing (numeric and filename)
- Added `update_conversation_title` to storage.py
- New endpoints: PATCH /api/conversations/{id}/title, GET /api/conversations/search
- Improved report generation with filename substitution

### Frontend
- Removed debugMode state and related code
- Added autocomplete dropdown component
- Implemented search functionality in Sidebar
- Enhanced ChatInterface with autocomplete and improved textarea sizing
- Updated CSS for better contrast and responsive design

## Files Changed
- Backend: config.py, council.py, docs_context.py, main.py, storage.py
- Frontend: App.jsx, ChatInterface.jsx, Sidebar.jsx, and related CSS files
- Documentation: README.md, ARCHITECTURE.md, new docs/ directory
- Configuration: .env.example, Makefile
- Scripts: scripts/test_setup.py

## Breaking Changes
None - all changes are backward compatible

## Testing
- All existing tests pass
- New test-setup script validates conversation creation workflow
- Manual testing of autocomplete, search, and rename features
This commit is contained in:
Irina Levit 2025-12-28 15:32:00 -05:00
commit 3546c04348
79 changed files with 13531 additions and 0 deletions
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## Cursor Rules (Project Standards)
### Non-negotiables
- Never commit secrets. Never paste credentials into code, docs, or logs.
- Prefer env vars over hardcoding (see `.env.example`).
- Add/extend tests for any behavior change.
### Backend standards
- Use the provider abstraction (`backend/llm_client.py`) for all model calls.
- Keep request/response payloads explicit and logged safely (no secrets).
- Treat uploaded documents as untrusted input:
- enforce `.md` extension
- enforce size limits
- never execute content
### Frontend standards
- Keep API calls in `frontend/src/api.js`.
- Keep app state + orchestration in `frontend/src/App.jsx`.
- Components should be readable and small.
### Testing
- Backend: `make test-backend`
- Frontend: `make test-frontend`
### Architecture
- The app runs locally; the GPU VM runs an OpenAI-compatible inference server.
- The app calls the VM over HTTP (or via SSH tunnel) and does not "use the GPU" directly.

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root = true
[*]
charset = utf-8
end_of_line = lf
insert_final_newline = true
trim_trailing_whitespace = true
[*.py]
indent_style = space
indent_size = 4
[*.{js,jsx,ts,tsx,json,css,md}]
indent_style = space
indent_size = 2

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# ============================================================================
# LLM Council Configuration
# ============================================================================
# Copy this file to .env and customize as needed
# ============================================================================
# ----------------------------------------------------------------------------
# Provider & Server Configuration
# ----------------------------------------------------------------------------
# Use local Ollama (automatically sets base URL to http://localhost:11434)
USE_LOCAL_OLLAMA=true
# For remote Ollama or other OpenAI-compatible servers, comment USE_LOCAL_OLLAMA
# and set the base URL instead:
# OPENAI_COMPAT_BASE_URL=http://your-server:11434
# OPENAI_COMPAT_BASE_URL=http://10.0.30.63:8000
# Optional API key if your server requires authentication
# OPENAI_COMPAT_API_KEY=your_api_key_here
# ----------------------------------------------------------------------------
# Model Configuration
# ----------------------------------------------------------------------------
# Models for the council (comma or newline separated)
# Tip: Check available models with: curl -s 'http://localhost:8001/api/llm/status?probe=true'
COUNCIL_MODELS=llama3.2:3b,qwen2.5:3b,gemma2:2b
# Chairman model (synthesizes final response from council)
CHAIRMAN_MODEL=llama3.2:3b
# Maximum tokens per request
MAX_TOKENS=1024
# ----------------------------------------------------------------------------
# Timeout Configuration
# ----------------------------------------------------------------------------
# Default timeout for general LLM queries (Stage 1: council responses)
LLM_TIMEOUT_SECONDS=300.0
# Timeout for chairman synthesis (may need longer for complex responses)
CHAIRMAN_TIMEOUT_SECONDS=180.0
# Timeout for title generation (short responses)
TITLE_GENERATION_TIMEOUT_SECONDS=120.0
# HTTP client timeout for OpenAI-compatible server (fallback, rarely used)
OPENAI_COMPAT_TIMEOUT_SECONDS=300
# ----------------------------------------------------------------------------
# Retry Configuration
# ----------------------------------------------------------------------------
# Number of retries for failed requests (retryable HTTP errors)
OPENAI_COMPAT_RETRIES=2
# Exponential backoff base delay between retries (seconds)
OPENAI_COMPAT_RETRY_BACKOFF_SECONDS=0.5
# ----------------------------------------------------------------------------
# Concurrency Configuration
# ----------------------------------------------------------------------------
# Maximum concurrent model requests (0 = unlimited, 1 = sequential)
LLM_MAX_CONCURRENCY=1
# ----------------------------------------------------------------------------
# Document Upload Configuration (Optional)
# ----------------------------------------------------------------------------
# Directory for uploaded markdown documents (per-conversation)
# DOCS_DIR=data/docs
# Maximum document size in bytes (default: 1MB)
# MAX_DOC_BYTES=1000000
# Maximum characters to preview when fetching documents (default: 20000)
# MAX_DOC_PREVIEW_CHARS=20000

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name: CI
on:
push:
branches: [main, master]
pull_request:
branches: [main, master]
jobs:
backend-test:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.10'
- name: Install uv
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.cargo/bin" >> $GITHUB_PATH
- name: Install dependencies
run: uv sync
- name: Run backend tests
run: uv run python -m unittest discover -s backend/tests -p "test_*.py" -v
frontend-test:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Set up Node.js
uses: actions/setup-node@v4
with:
node-version: '20'
- name: Install dependencies
run: |
cd frontend
npm ci
- name: Run frontend tests
run: |
cd frontend
npm test
- name: Lint frontend
run: |
cd frontend
npm run lint
lint-python:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: '3.10'
- name: Install uv
run: |
curl -LsSf https://astral.sh/uv/install.sh | sh
echo "$HOME/.cargo/bin" >> $GITHUB_PATH
- name: Install dependencies
run: uv sync
- name: Check Python syntax
run: |
python3 -m py_compile backend/**/*.py || true
echo "Python syntax check complete"
secret-scanning:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Run Gitleaks
uses: gitleaks/gitleaks-action@v2
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
continue-on-error: true
dependency-scan:
runs-on: ubuntu-latest
steps:
- name: Check out code
uses: actions/checkout@v4
- name: Run Trivy vulnerability scanner
uses: aquasecurity/trivy-action@master
with:
scan-type: 'fs'
scan-ref: '.'
format: 'sarif'
output: 'trivy-results.sarif'
severity: 'CRITICAL,HIGH'
continue-on-error: true
- name: Upload Trivy results to GitHub Security
uses: github/codeql-action/upload-sarif@v3
if: always()
with:
sarif_file: 'trivy-results.sarif'
continue-on-error: true
workflow-summary:
runs-on: ubuntu-latest
needs: [backend-test, frontend-test, lint-python, secret-scanning, dependency-scan]
if: always()
steps:
- name: Generate workflow summary
run: |
echo "## 🔍 CI Workflow Summary" >> $GITHUB_STEP_SUMMARY || true
echo "" >> $GITHUB_STEP_SUMMARY || true
echo "### Job Results" >> $GITHUB_STEP_SUMMARY || true
echo "" >> $GITHUB_STEP_SUMMARY || true
echo "| Job | Status |" >> $GITHUB_STEP_SUMMARY || true
echo "|-----|--------|" >> $GITHUB_STEP_SUMMARY || true
echo "| 🐍 Backend Tests | ${{ needs.backend-test.result }} |" >> $GITHUB_STEP_SUMMARY || true
echo "| ⚛️ Frontend Tests | ${{ needs.frontend-test.result }} |" >> $GITHUB_STEP_SUMMARY || true
echo "| 📝 Python Lint | ${{ needs.lint-python.result }} |" >> $GITHUB_STEP_SUMMARY || true
echo "| 🔐 Secret Scanning | ${{ needs.secret-scanning.result }} |" >> $GITHUB_STEP_SUMMARY || true
echo "| 📦 Dependency Scan | ${{ needs.dependency-scan.result }} |" >> $GITHUB_STEP_SUMMARY || true
echo "" >> $GITHUB_STEP_SUMMARY || true
echo "### 📊 Summary" >> $GITHUB_STEP_SUMMARY || true
echo "" >> $GITHUB_STEP_SUMMARY || true
echo "All checks have completed. Review individual job logs for details." >> $GITHUB_STEP_SUMMARY || true
continue-on-error: true

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# Python-generated files
__pycache__/
*.py[oc]
*.pyc
build/
dist/
wheels/
*.egg-info
.pytest_cache/
.coverage
htmlcov/
# Virtual environments
.venv
venv/
ENV/
env/
# Keys and secrets
.env
.env.local
.env.*.local
# Data files
data/
# Frontend
frontend/node_modules/
frontend/dist/
frontend/.vite/
frontend/.vite/
frontend/build/
# IDE
.vscode/
.idea/
*.swp
*.swo
*~
# OS
.DS_Store
Thumbs.db
# Logs
*.log
logs/
# Temporary files
*.tmp
*.temp
.cache/

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3.10

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## Architecture
### Overview
LLM Council is a local web app with:
- **Frontend**: React + Vite (`frontend/`) on `:5173`
- **Backend**: FastAPI (`backend/`) on `:8001`
- **Storage**: JSON conversations + uploaded markdown docs on disk (`data/`)
- **LLM Provider**: pluggable backend client
### Runtime data flow
1. UI sends a message to backend (`/api/conversations/{id}/message/stream`).
2. Backend loads any uploaded markdown docs for the conversation and injects them as additional context.
3. Backend runs a 3-stage pipeline:
- **Stage 1**: query each council model in parallel
- **Stage 2**: anonymized peer review + ranking
- **Stage 3**: chairman synthesis
### LLM provider layer
The backend uses OpenAI-compatible API servers (Ollama, vLLM, TGI, etc.).
Configuration:
- `USE_LOCAL_OLLAMA=true` - automatically sets base URL to `http://localhost:11434`
- `OPENAI_COMPAT_BASE_URL` - set to your server URL (e.g., `http://remote-server:11434`)
The provider (`backend/openai_compat.py`) targets servers that expose:
- `POST /v1/chat/completions`
- `GET /v1/models`
The council orchestration uses the unified interface in `backend/llm_client.py`.
### Document uploads and references
- Per-conversation markdown documents are stored under: `data/docs/<conversation_id>/`
- Documents are automatically numbered (1, 2, 3, etc.) based on upload order
- Documents can be referenced in prompts using:
- Numeric references: `@1`, `@2`, `@3` (by upload order)
- Filename references: `@filename` (fuzzy matching)
- Backend endpoints:
- `GET /api/conversations/{id}/documents`
- `POST /api/conversations/{id}/documents` (multipart file)
- `GET /api/conversations/{id}/documents/{doc_id}` (preview/truncated)
- `DELETE /api/conversations/{id}/documents/{doc_id}`
- Document context is automatically injected when referenced in user queries
- Export reports replace `@1`, `@2` references with actual filenames
### Conversation management
- Conversations stored as JSON files in `data/conversations/`
- Features:
- Create, list, get, delete conversations
- Rename conversations (inline editing)
- Search conversations by title and message content
- Export conversations as markdown reports
- Auto-generate titles from first message
### Frontend features
- **Document autocomplete**: Type `@` to see numbered document list with autocomplete
- **Conversation search**: Search box filters conversations by title/content
- **Theme toggle**: Light/dark mode support
- **Streaming responses**: Real-time updates as models respond
- **Document preview**: View uploaded documents inline
- **Export reports**: Download conversations as markdown files
### Configuration
Primary runtime config is via `.env` (gitignored). Key settings:
- Model configuration: `COUNCIL_MODELS`, `CHAIRMAN_MODEL`
- Timeouts: `LLM_TIMEOUT_SECONDS`, `CHAIRMAN_TIMEOUT_SECONDS`, `OPENAI_COMPAT_TIMEOUT_SECONDS`
- HTTP client timeouts: `OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS`, `OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS`, `OPENAI_COMPAT_POOL_TIMEOUT_SECONDS`
- Document limits: `MAX_DOC_BYTES`, `MAX_DOC_PREVIEW_CHARS`
Useful endpoints:
- `GET /api/llm/status` and `GET /api/llm/status?probe=true`
- `GET /api/conversations/search?q=...` - Search conversations
- `PATCH /api/conversations/{id}/title` - Rename conversation
### Security model (local dev)
This is currently built for local/private network usage.
If you deploy beyond localhost, add:
- auth (session/token)
- rate limits
- upload limits
- network restrictions / TLS

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# Changelog
All notable changes to this project will be documented in this file.
## [Unreleased] - Production Ready Release
### Added
- **Document Reference System**: Numbered references (@1, @2, etc.) with autocomplete dropdown
- **Fuzzy Filename Matching**: Support for @filename references with partial matching
- **Conversation Rename**: Inline editing to rename conversations
- **Conversation Search**: Search by title and content, always-visible search box
- **Autocomplete Dropdown**: Keyboard-navigable dropdown when typing @
- **Test Setup Script**: `make test-setup` for automated test conversation creation
- **Configuration Template**: `.env.example` with all available options
- **HTTP Client Timeouts**: Configurable connect, write, and pool timeouts
- **Comprehensive Documentation**: Deployment guides, GPU VM setup, architecture docs
### Changed
- **UI Improvements**: Better text contrast, larger input textarea, improved document display
- **Report Generation**: @N references replaced with actual filenames in exports
- **Document Filtering**: Prioritizes numeric refs > filename refs > all documents
- **Removed Debug UI**: Cleaned up debug toggle button
- **Documentation Organization**: Moved deployment docs to `docs/` directory
### Fixed
- **Text Visibility**: Improved contrast in dark mode
- **Input Sizing**: Textarea now expands to full available width
- **File Text Color**: Better visibility for document names
- **Search Visibility**: Search box remains visible even with no conversations
- **ReferenceError**: Fixed `debugMode is not defined` error
### Technical
- Enhanced `docs_context.py` with reference parsing
- New API endpoints: `PATCH /api/conversations/{id}/title`, `GET /api/conversations/search`
- Improved error handling and user feedback
- Better state management in React components
## [0.1.0] - Initial Release
### Added
- Multi-LLM council system with Stage 1 (individual responses), Stage 2 (peer review), Stage 3 (synthesis)
- OpenAI-compatible API support (Ollama, vLLM, TGI)
- Document upload and management
- Conversation management
- Streaming responses
- Light/dark theme toggle
- Basic UI with React + Vite

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feat: Major UI/UX improvements and production readiness
## Features Added
### Document Reference System
- Implemented numbered document references (@1, @2, etc.) with autocomplete dropdown
- Added fuzzy filename matching for @filename references
- Document filtering now prioritizes numeric refs > filename refs > all documents
- Autocomplete dropdown appears when typing @ with keyboard navigation (Up/Down, Enter/Tab, Escape)
- Document numbers displayed in UI for easy reference
### Conversation Management
- Added conversation rename functionality with inline editing
- Implemented conversation search (by title and content)
- Search box always visible, even when no conversations exist
- Export reports now replace @N references with actual filenames
### UI/UX Improvements
- Removed debug toggle button
- Improved text contrast in dark mode (better visibility)
- Made input textarea expand to full available width
- Fixed file text color for better readability
- Enhanced document display with numbered badges
### Configuration & Timeouts
- Made HTTP client timeouts configurable (connect, write, pool)
- Added .env.example with all configuration options
- Updated timeout documentation
### Developer Experience
- Added `make test-setup` target for automated test conversation creation
- Test setup script supports TEST_MESSAGE and TEST_DOCS env vars
- Improved Makefile with dev and test-setup targets
### Documentation
- Updated ARCHITECTURE.md with all new features
- Created comprehensive deployment documentation
- Added GPU VM setup guides
- Removed unnecessary markdown files (CLAUDE.md, CONTRIBUTING.md, header.jpg)
- Organized documentation in docs/ directory
### GPU VM / Ollama (Stability + GPU Offload)
- Updated GPU VM docs to reflect the working systemd environment for remote Ollama
- Standardized remote Ollama port to 11434 (and added /v1/models verification)
- Documented required env for GPU offload on this VM:
- `OLLAMA_MODELS=/mnt/data/ollama`, `HOME=/mnt/data/ollama/home`
- `OLLAMA_LLM_LIBRARY=cuda_v12` (not `cuda`)
- `LD_LIBRARY_PATH=/usr/local/lib/ollama:/usr/local/lib/ollama/cuda_v12`
## Technical Changes
### Backend
- Enhanced `docs_context.py` with reference parsing (numeric and filename)
- Added `update_conversation_title` to storage.py
- New endpoints: PATCH /api/conversations/{id}/title, GET /api/conversations/search
- Improved report generation with filename substitution
### Frontend
- Removed debugMode state and related code
- Added autocomplete dropdown component
- Implemented search functionality in Sidebar
- Enhanced ChatInterface with autocomplete and improved textarea sizing
- Updated CSS for better contrast and responsive design
## Files Changed
- Backend: config.py, council.py, docs_context.py, main.py, storage.py
- Frontend: App.jsx, ChatInterface.jsx, Sidebar.jsx, and related CSS files
- Documentation: README.md, ARCHITECTURE.md, new docs/ directory
- Configuration: .env.example, Makefile
- Scripts: scripts/test_setup.py
## Breaking Changes
None - all changes are backward compatible
## Testing
- All existing tests pass
- New test-setup script validates conversation creation workflow
- Manual testing of autocomplete, search, and rename features

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.PHONY: dev backend frontend test test-backend test-frontend test-setup \
stop-backend stop-frontend stop-ollama reset \
start-ollama start-backend start-frontend up restart \
status logs help
help:
@# Use color only when output is a TTY. Keep plain output for logs/CI.
@is_tty=0; if [ -t 1 ]; then is_tty=1; fi; \
if [ $$is_tty -eq 1 ]; then \
BOLD=$$(printf '\033[1m'); DIM=$$(printf '\033[2m'); RESET=$$(printf '\033[0m'); \
CYAN=$$(printf '\033[36m'); GREEN=$$(printf '\033[32m'); YELLOW=$$(printf '\033[33m'); \
else \
BOLD=""; DIM=""; RESET=""; CYAN=""; GREEN=""; YELLOW=""; \
fi; \
printf "%sLLM Council%s %sMake targets%s\n" "$$BOLD" "$$RESET" "$$DIM" "$$RESET"; \
printf "\n"; \
printf "%sCore%s\n" "$$CYAN" "$$RESET"; \
printf " %smake dev%s Start backend + frontend (foreground, Ctrl+C to stop)\n" "$$GREEN" "$$RESET"; \
printf " %smake up%s Start ollama + backend + frontend (background via nohup)\n" "$$GREEN" "$$RESET"; \
printf " %smake restart%s Reset (stop) then start everything (up)\n" "$$GREEN" "$$RESET"; \
printf "\n"; \
printf "%sStart/Stop%s\n" "$$CYAN" "$$RESET"; \
printf " %smake start-ollama%s Start Ollama (systemd if available, else 'ollama serve' nohup)\n" "$$GREEN" "$$RESET"; \
printf " %smake start-backend%s Start backend (nohup, logs: %s/tmp/llm-council-backend.log%s)\n" "$$GREEN" "$$RESET" "$$DIM" "$$RESET"; \
printf " %smake start-frontend%s Start frontend (nohup, logs: %s/tmp/llm-council-frontend.log%s)\n" "$$GREEN" "$$RESET" "$$DIM" "$$RESET"; \
printf " %smake stop-backend%s Stop backend on port %s8001%s\n" "$$GREEN" "$$RESET" "$$YELLOW" "$$RESET"; \
printf " %smake stop-frontend%s Stop frontend on port %s5173%s\n" "$$GREEN" "$$RESET" "$$YELLOW" "$$RESET"; \
printf " %smake stop-ollama%s Attempt to stop Ollama (systemd if available)\n" "$$GREEN" "$$RESET"; \
printf " %smake reset%s Stop frontend + backend + attempt Ollama stop\n" "$$GREEN" "$$RESET"; \
printf "\n"; \
printf "%sTesting / setup%s\n" "$$CYAN" "$$RESET"; \
printf " %smake test%s Run backend + frontend tests\n" "$$GREEN" "$$RESET"; \
printf " %smake test-backend%s Run backend unit tests\n" "$$GREEN" "$$RESET"; \
printf " %smake test-frontend%s Run frontend tests\n" "$$GREEN" "$$RESET"; \
printf " %smake test-setup%s Create a new conversation, upload TEST_DOCS, prefill TEST_MESSAGE in UI\n" "$$GREEN" "$$RESET"; \
printf " %sEnv:%s TEST_DOCS=path1.md,path2.md TEST_MESSAGE='your msg'\n" "$$DIM" "$$RESET"; \
printf "\n"; \
printf "%sDiagnostics%s\n" "$$CYAN" "$$RESET"; \
printf " %smake status%s Show whether backend/frontend/ollama are up + PIDs + basic info\n" "$$GREEN" "$$RESET"; \
printf " %smake logs%s Tail recent backend/frontend logs (from /tmp)\n" "$$GREEN" "$$RESET"; \
printf "\n"; \
printf "%sHelp%s\n" "$$CYAN" "$$RESET"; \
printf " %smake help%s Show this message\n" "$$GREEN" "$$RESET"
backend:
uv run python -m backend.main
frontend:
cd frontend && npm run dev
dev:
./start.sh
test-backend:
uv run python -m unittest discover -s backend/tests -p "test_*.py" -q
test-frontend:
cd frontend && npm test
test: test-backend test-frontend
stop-backend:
@echo "Stopping backend processes on port 8001..."
@if lsof -ti:8001 > /dev/null 2>&1; then \
lsof -ti:8001 | xargs kill -9 2>/dev/null || true; \
echo "✓ Backend stopped"; \
sleep 1; \
else \
echo "✓ No backend process found on port 8001"; \
fi
stop-frontend:
@echo "Stopping frontend processes on port 5173..."
@if lsof -ti:5173 > /dev/null 2>&1; then \
lsof -ti:5173 | xargs kill -9 2>/dev/null || true; \
echo "✓ Frontend stopped"; \
sleep 1; \
else \
echo "✓ No frontend process found on port 5173"; \
fi
stop-ollama:
@echo "Stopping Ollama..."
@# Prefer systemd if available; do not force-kill processes (safer).
@if command -v systemctl >/dev/null 2>&1; then \
if systemctl is-active --quiet ollama 2>/dev/null; then \
echo "Stopping systemd service: ollama"; \
systemctl stop ollama 2>/dev/null || true; \
sleep 2; \
else \
echo "Ollama systemd service is not active"; \
fi; \
else \
echo "systemctl not found (not a systemd environment)"; \
fi
@PIDS=$$(pgrep -x ollama 2>/dev/null || true); \
if [ -n "$$PIDS" ]; then \
echo "⚠️ Ollama processes still running (PIDs: $$PIDS)"; \
echo " Stop manually with one of:"; \
echo " - systemctl stop ollama (if installed as a service)"; \
echo " - pkill -x ollama"; \
echo " - sudo pkill -x ollama"; \
else \
echo "✓ Ollama is stopped"; \
fi
reset: stop-frontend stop-backend stop-ollama
@echo ""
@echo "✓ Reset complete - Frontend/Backend stopped (Ollama stop attempted)"
@echo ""
@echo "To start fresh (recommended):"
@echo " make up"
@echo ""
@echo "Or manually:"
@echo " 1) make start-ollama"
@echo " 2) make start-backend"
@echo " 3) make start-frontend"
start-ollama:
@echo "Starting Ollama..."
@if curl -s --max-time 2 http://localhost:11434/api/tags >/dev/null 2>&1; then \
echo "✓ Ollama already responding on http://localhost:11434"; \
exit 0; \
fi
@if command -v systemctl >/dev/null 2>&1 && systemctl list-unit-files 2>/dev/null | grep -q "^ollama\\.service"; then \
echo "Starting systemd service: ollama"; \
systemctl start ollama 2>/dev/null || true; \
sleep 2; \
else \
echo "systemd service not available; starting 'ollama serve' in background"; \
nohup ollama serve > /tmp/llm-council-ollama.log 2>&1 & \
echo "ollama serve PID: $$!"; \
sleep 2; \
fi
@if curl -s --max-time 2 http://localhost:11434/api/tags >/dev/null 2>&1; then \
echo "✓ Ollama is up: http://localhost:11434"; \
else \
echo "⚠️ Ollama did not respond yet. Check: /tmp/llm-council-ollama.log"; \
fi
start-backend:
@echo "Starting backend..."
@if curl -s --max-time 2 http://localhost:8001/ >/dev/null 2>&1; then \
echo "✓ Backend already responding on http://localhost:8001"; \
exit 0; \
fi
@nohup uv run python -m backend.main > /tmp/llm-council-backend.log 2>&1 & \
echo "backend PID: $$!"; \
sleep 2; \
if curl -s --max-time 2 http://localhost:8001/ >/dev/null 2>&1; then \
echo "✓ Backend is up: http://localhost:8001"; \
else \
echo "⚠️ Backend did not respond yet. Check: /tmp/llm-council-backend.log"; \
fi
start-frontend:
@echo "Starting frontend..."
@if curl -s --max-time 2 http://localhost:5173/ >/dev/null 2>&1; then \
echo "✓ Frontend already responding on http://localhost:5173"; \
exit 0; \
fi
@nohup sh -c "cd frontend && npm run dev" > /tmp/llm-council-frontend.log 2>&1 & \
echo "frontend PID: $$!"; \
sleep 2; \
if curl -s --max-time 2 http://localhost:5173/ >/dev/null 2>&1; then \
echo "✓ Frontend is up: http://localhost:5173"; \
else \
echo "⚠️ Frontend did not respond yet. Check: /tmp/llm-council-frontend.log"; \
fi
up: start-ollama start-backend start-frontend
@echo ""
@echo "✓ All services started (or already running)"
@echo "Frontend: http://localhost:5173"
@echo "Backend: http://localhost:8001"
@echo "Ollama: http://localhost:11434"
restart: reset up
status:
@echo "=== LLM Council status ==="
@date
@echo ""
@echo "-- Backend (8001) --"
@if curl -s --max-time 2 http://localhost:8001/ >/dev/null 2>&1; then \
echo "✓ UP: http://localhost:8001"; \
else \
echo "✗ DOWN: http://localhost:8001"; \
fi
@echo "PIDs:"; lsof -ti:8001 2>/dev/null | tr '\n' ' ' || true; echo ""
@echo ""
@echo "-- Frontend (5173) --"
@if curl -s --max-time 2 http://localhost:5173/ >/dev/null 2>&1; then \
echo "✓ UP: http://localhost:5173"; \
else \
echo "✗ DOWN: http://localhost:5173"; \
fi
@echo "PIDs:"; lsof -ti:5173 2>/dev/null | tr '\n' ' ' || true; echo ""
@echo ""
@echo "-- Ollama (11434) --"
@if curl -s --max-time 2 http://localhost:11434/api/tags >/dev/null 2>&1; then \
echo "✓ UP: http://localhost:11434"; \
else \
echo "✗ DOWN/UNREACHABLE: http://localhost:11434"; \
fi
@O_PID=$$(pgrep -x ollama 2>/dev/null || true); \
R_CNT=$$(pgrep -f "ollama runner" 2>/dev/null | wc -l | tr -d ' '); \
if [ -n "$$O_PID" ]; then \
echo "ollama PIDs: $$O_PID"; \
echo "runner count: $$R_CNT"; \
echo ""; \
echo "top ollama processes:"; \
ps -o pid,ppid,pcpu,pmem,etime,command -p $$O_PID 2>/dev/null || true; \
pgrep -f "ollama runner" 2>/dev/null | head -5 | while read pid; do \
ps -o pid,ppid,pcpu,pmem,etime,command -p $$pid 2>/dev/null || true; \
done; \
else \
echo "ollama not running"; \
fi
@echo ""
@echo "-- Quick health hints --"
@echo "Backend log: /tmp/llm-council-backend.log"
@echo "Frontend log: /tmp/llm-council-frontend.log"
@echo "Run: make logs"
logs:
@echo "=== Recent logs ==="
@echo ""
@echo "-- Backend log (/tmp/llm-council-backend.log) --"
@tail -n 80 /tmp/llm-council-backend.log 2>/dev/null || echo "(no backend log found)"
@echo ""
@echo "-- Frontend log (/tmp/llm-council-frontend.log) --"
@tail -n 120 /tmp/llm-council-frontend.log 2>/dev/null || echo "(no frontend log found)"
test-setup:
@echo "Setting up test conversation..."
@echo "Usage: TEST_MESSAGE='your message' TEST_DOCS='path1.md,path2.md' make test-setup"
@echo ""
@echo "⚠️ Safeguard check: Verifying backend is running..."
@if ! curl -s http://localhost:8001/ > /dev/null 2>&1; then \
echo "✗ Backend is NOT running on port 8001"; \
echo " Starting backend in background..."; \
uv run python -m backend.main > /tmp/llm-council-backend.log 2>&1 & \
BACKEND_PID=$$!; \
echo " Backend PID: $$BACKEND_PID"; \
sleep 2; \
if ! curl -s http://localhost:8001/ > /dev/null 2>&1; then \
echo "✗ Backend failed to start"; \
exit 1; \
fi; \
echo "✓ Backend started"; \
else \
echo "✓ Backend is already running"; \
fi
@echo ""
@echo "Running test setup script..."
@uv run python scripts/test_setup.py > /tmp/llm-council-test-setup.log 2>&1; \
cat /tmp/llm-council-test-setup.log; \
CONV_ID=$$(grep "CONVERSATION_ID=" /tmp/llm-council-test-setup.log | cut -d'=' -f2 | head -1); \
OPEN_URL=$$(grep "OPEN_URL=" /tmp/llm-council-test-setup.log | cut -d'=' -f2- | head -1); \
echo ""; \
echo "Checking frontend status..."; \
if pgrep -f "vite" > /dev/null 2>&1 || pgrep -f "npm.*dev" > /dev/null 2>&1; then \
echo "✓ Frontend process detected (already running)"; \
elif timeout 2 curl -s http://localhost:5173/ > /dev/null 2>&1; then \
echo "✓ Frontend is responding"; \
else \
echo "Starting frontend in background..."; \
cd frontend && npm run dev > /tmp/llm-council-frontend.log 2>&1 & \
echo "Frontend starting (PID: $$!)"; \
echo "Waiting up to 10 seconds for frontend..."; \
for i in 1 2 3 4 5 6 7 8 9 10; do \
if timeout 1 curl -s http://localhost:5173/ > /dev/null 2>&1; then \
echo "✓ Frontend is ready!"; \
break; \
fi; \
sleep 1; \
done; \
fi; \
echo ""; \
echo "✓ Test setup complete!"; \
echo ""; \
if [ -n "$$OPEN_URL" ]; then \
echo "Opening browser: $$OPEN_URL"; \
xdg-open "$$OPEN_URL" 2>/dev/null || \
open "$$OPEN_URL" 2>/dev/null || \
echo "Open manually: $$OPEN_URL"; \
elif [ -n "$$CONV_ID" ]; then \
echo "Opening browser with new conversation: $$CONV_ID"; \
xdg-open "http://localhost:5173/?conversation=$$CONV_ID" 2>/dev/null || \
open "http://localhost:5173/?conversation=$$CONV_ID" 2>/dev/null || \
echo "Open manually: http://localhost:5173/?conversation=$$CONV_ID"; \
else \
echo "Open: http://localhost:5173"; \
echo "The new conversation should appear in the sidebar"; \
fi; \
echo ""; \
echo "Note: TEST_MESSAGE is NOT sent automatically - check the script output above for the message to paste"

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# LLM Council
The idea of this repo is that instead of asking a question to a single LLM, you can group multiple LLMs into your "LLM Council". This repo is a simple, local web app that essentially looks like ChatGPT except it sends your query to multiple LLMs via an OpenAI-compatible API (Ollama, vLLM, TGI, etc.), it then asks them to review and rank each other's work, and finally a Chairman LLM produces the final response.
In a bit more detail, here is what happens when you submit a query:
1. **Stage 1: First opinions**. The user query is given to all LLMs individually, and the responses are collected. The individual responses are shown in a "tab view", so that the user can inspect them all one by one.
2. **Stage 2: Review**. Each individual LLM is given the responses of the other LLMs. Under the hood, the LLM identities are anonymized so that the LLM can't play favorites when judging their outputs. The LLM is asked to rank them in accuracy and insight.
3. **Stage 3: Final response**. The designated Chairman of the LLM Council takes all of the model's responses and compiles them into a single final answer that is presented to the user.
## Vibe Code Alert
This project was 99% vibe coded as a fun Saturday hack because I wanted to explore and evaluate a number of LLMs side by side in the process of [reading books together with LLMs](https://x.com/karpathy/status/1990577951671509438). It's nice and useful to see multiple responses side by side, and also the cross-opinions of all LLMs on each other's outputs. You're not going to support it in any way, it's provided here as is for other people's inspiration and you don't intend to improve it. Code is ephemeral now and libraries are over, ask your LLM to change it in whatever way you like.
## Setup
### 1. Install Dependencies
The project uses [uv](https://docs.astral.sh/uv/) for project management.
**Backend:**
```bash
uv sync
```
**Frontend:**
```bash
cd frontend
npm install
cd ..
```
### 2. Configure Ollama Server
LLM Council requires an OpenAI-compatible API server. The easiest way to get started is with Ollama running locally or on a remote server.
**For local Ollama:**
1. Install and start Ollama: https://ollama.ai
2. Pull some models:
```bash
ollama pull llama3.2:3b
ollama pull qwen2.5:3b
ollama pull gemma2:2b
```
### 3. Configure Environment
Create a `.env` file in the project root with your configuration:
**For local Ollama:**
```bash
USE_LOCAL_OLLAMA=true
COUNCIL_MODELS=llama3.2:3b,qwen2.5:3b,gemma2:2b
CHAIRMAN_MODEL=llama3.2:3b
MAX_TOKENS=1024
LLM_MAX_CONCURRENCY=1
```
**For remote Ollama or other OpenAI-compatible server:**
```bash
OPENAI_COMPAT_BASE_URL=http://your-server:11434
COUNCIL_MODELS=llama3.2:3b,qwen2.5:3b,gemma2:2b
CHAIRMAN_MODEL=llama3.2:3b
MAX_TOKENS=2048
LLM_MAX_CONCURRENCY=1
```
**Optional timeout configuration:**
```bash
LLM_TIMEOUT_SECONDS=120.0 # Default timeout for LLM queries
CHAIRMAN_TIMEOUT_SECONDS=180.0 # Timeout for chairman synthesis
TITLE_GENERATION_TIMEOUT_SECONDS=120.0 # Timeout for title generation
OPENAI_COMPAT_TIMEOUT_SECONDS=300.0 # Timeout for OpenAI-compatible server
OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS=10.0 # HTTP connection timeout
OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS=10.0 # HTTP write timeout
OPENAI_COMPAT_POOL_TIMEOUT_SECONDS=10.0 # HTTP pool timeout
```
See `.env.example` for all available configuration options. Alternatively, you can edit `backend/config.py` directly to set defaults.
## Running the Application
**Option 1: Use the start script**
```bash
./start.sh
```
**Option 2: Use Makefile**
```bash
make dev
```
**Option 3: Run manually**
Terminal 1 (Backend):
```bash
uv run python -m backend.main
```
Terminal 2 (Frontend):
```bash
cd frontend
npm run dev
```
Then open http://localhost:5173 in your browser.
**Option 4: Test setup with pre-configured conversation**
```bash
# Set in .env:
# TEST_MESSAGE="Your message"
# TEST_DOCS="doc1.md,doc2.md"
make test-setup
```
This creates a new conversation with today's date/time, uploads documents, and **pre-fills** the message in the UI (it does **not** auto-send).
### Frontend theme default (optional)
By default, the UI theme is persisted in `localStorage`. If there is no saved theme yet, you can set a default theme via a Vite env var:
```bash
# Example (starts in dark mode if there's no localStorage value yet)
VITE_DEFAULT_THEME=dark make dev
```
## Using Ollama on a Remote Server
If you have Ollama running on a remote server or VM:
1. In your project `.env`, set:
```bash
OPENAI_COMPAT_BASE_URL=http://your-server-ip:11434
COUNCIL_MODELS=llama3.2:3b,qwen2.5:3b,gemma2:2b
CHAIRMAN_MODEL=llama3.2:3b
MAX_TOKENS=2048
LLM_MAX_CONCURRENCY=1
```
2. Verify connectivity from your machine:
```bash
curl http://your-server-ip:11434/api/tags
```
## Using Other OpenAI-Compatible Servers (vLLM, TGI, etc.)
If you're running vLLM, TGI, or another OpenAI-compatible server:
1. Ensure your server exposes:
- `POST /v1/chat/completions`
- `GET /v1/models`
2. In your project `.env`, set:
```bash
OPENAI_COMPAT_BASE_URL=http://your-server:port
COUNCIL_MODELS=your-model-1,your-model-2,your-model-3
CHAIRMAN_MODEL=your-model-1
MAX_TOKENS=2048
LLM_MAX_CONCURRENCY=1
# (optional) if your server requires auth:
# OPENAI_COMPAT_API_KEY=...
```
3. Verify connectivity:
```bash
curl http://your-server:port/v1/models
```
## Documentation
- **[Architecture](ARCHITECTURE.md)** - System architecture and design
- **[Deployment Guide](docs/DEPLOYMENT.md)** - How to deploy with remote GPU VM
- **[Deployment Recommendations](docs/DEPLOYMENT_RECOMMENDATIONS.md)** - Professional deployment options
## Tech Stack
- **Backend:** FastAPI (Python 3.10+), async httpx, OpenAI-compatible API
- **Frontend:** React + Vite, react-markdown for rendering
- **Storage:** JSON files in `data/conversations/`
- **Package Management:** uv for Python, npm for JavaScript
- **LLM Backend:** Ollama, vLLM, TGI, or any OpenAI-compatible server

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"""LLM Council backend package."""

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"""Configuration for the LLM Council."""
import os
from dotenv import load_dotenv
load_dotenv()
# Helpers
def _parse_int_env(name: str, default: int) -> int:
raw = os.getenv(name)
if raw is None or raw.strip() == "":
return default
try:
return int(raw.strip())
except ValueError:
return default
def _parse_float_env(name: str, default: float) -> float:
raw = os.getenv(name)
if raw is None or raw.strip() == "":
return default
try:
return float(raw.strip())
except ValueError:
return default
def _parse_list_env(name: str) -> list[str] | None:
"""
Parses a list from an env var.
Supported formats:
- Comma-separated: "a,b,c"
- Newline-separated: "a\\nb\\nc"
"""
raw = os.getenv(name)
if raw is None:
return None
raw = raw.strip()
if raw == "":
return []
# Allow either commas or newlines.
parts = []
for chunk in raw.replace("\r\n", "\n").split("\n"):
parts.extend(chunk.split(","))
return [p.strip() for p in parts if p.strip()]
# Council members - list of model identifiers (Ollama model names)
# Can be overridden via env var COUNCIL_MODELS (comma or newline separated).
_DEFAULT_COUNCIL_MODELS = [
"llama3.2:3b",
"qwen2.5:3b",
"gemma2:2b",
]
COUNCIL_MODELS = _parse_list_env("COUNCIL_MODELS") or _DEFAULT_COUNCIL_MODELS
# Chairman model - synthesizes final response
CHAIRMAN_MODEL = os.getenv("CHAIRMAN_MODEL") or "llama3.2:3b"
# Maximum tokens per request
# Default: 2048 tokens (reasonable for most responses)
# Increase if you need longer responses
MAX_TOKENS = _parse_int_env("MAX_TOKENS", 2048)
# Request timeout configuration (in seconds)
# Default timeout for general LLM queries (Stage 1: council responses)
# Used by llm_client.py and passed to openai_compat.query_model()
LLM_TIMEOUT_SECONDS = _parse_float_env("LLM_TIMEOUT_SECONDS", 120.0)
# Timeout for chairman synthesis (may need longer for complex responses)
CHAIRMAN_TIMEOUT_SECONDS = _parse_float_env("CHAIRMAN_TIMEOUT_SECONDS", 180.0)
# Timeout for title generation (short responses)
TITLE_GENERATION_TIMEOUT_SECONDS = _parse_float_env("TITLE_GENERATION_TIMEOUT_SECONDS", 120.0)
# OpenAI-compatible provider tuning (Ollama / vLLM / TGI)
# If USE_LOCAL_OLLAMA=true, automatically set base URL to localhost:11434 (convenience flag)
if os.getenv("USE_LOCAL_OLLAMA", "").strip().lower() in ("true", "1", "yes"):
_openai_compat_base_url = "http://localhost:11434"
else:
_openai_compat_base_url = os.getenv("OPENAI_COMPAT_BASE_URL")
OPENAI_COMPAT_BASE_URL = _openai_compat_base_url
# HTTP client timeout (fallback when timeout not explicitly passed to openai_compat functions)
# Used by: list_models() and as fallback in query_model() if called directly without timeout
# Should be >= LLM_TIMEOUT_SECONDS for safety, but list_models() is fast so can be lower
OPENAI_COMPAT_TIMEOUT_SECONDS = _parse_float_env("OPENAI_COMPAT_TIMEOUT_SECONDS", 300.0)
# HTTP client connection timeout (time to establish connection)
OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS = _parse_float_env("OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS", 10.0)
# HTTP client write timeout (time to send request)
OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS = _parse_float_env("OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS", 10.0)
# HTTP client pool timeout (time to get connection from pool)
OPENAI_COMPAT_POOL_TIMEOUT_SECONDS = _parse_float_env("OPENAI_COMPAT_POOL_TIMEOUT_SECONDS", 10.0)
# Number of retries for failed requests (retryable HTTP errors: 408, 409, 425, 429, 500, 502, 503, 504)
OPENAI_COMPAT_RETRIES = _parse_int_env("OPENAI_COMPAT_RETRIES", 2)
# Exponential backoff base delay between retries (seconds) - actual delay is backoff * (2^attempt)
OPENAI_COMPAT_RETRY_BACKOFF_SECONDS = _parse_float_env("OPENAI_COMPAT_RETRY_BACKOFF_SECONDS", 0.5)
# Debug mode - show debug logs in console (set DEBUG=true in .env)
DEBUG = os.getenv("DEBUG", "").strip().lower() in ("true", "1", "yes")
# Markdown uploads (per-conversation)
DOCS_DIR = os.getenv("DOCS_DIR") or "data/docs"
MAX_DOC_BYTES = _parse_int_env("MAX_DOC_BYTES", 1_000_000) # 1MB
MAX_DOC_PREVIEW_CHARS = _parse_int_env("MAX_DOC_PREVIEW_CHARS", 20_000)
# Data directory for conversation storage
DATA_DIR = "data/conversations"

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"""3-stage LLM Council orchestration."""
import time
from typing import List, Dict, Any, Tuple, Optional
from .llm_client import query_models_parallel, query_model
from .config import COUNCIL_MODELS, CHAIRMAN_MODEL, CHAIRMAN_TIMEOUT_SECONDS, TITLE_GENERATION_TIMEOUT_SECONDS
def _format_docs_context(docs_text: Optional[str]) -> str:
if not docs_text or not docs_text.strip():
return ""
return (
"\n\nREFERENCE DOCUMENTS (user-provided markdown):\n"
"Use these as additional context if relevant. Quote sparingly and cite sections when helpful.\n"
f"{docs_text.strip()}\n"
)
async def stage1_collect_responses(user_query: str, docs_text: Optional[str] = None) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
"""
Stage 1: Collect individual responses from all council models.
Args:
user_query: The user's question
Returns:
Tuple of (results list, metadata dict with timing info)
"""
start_time = time.time()
prompt = f"{user_query}{_format_docs_context(docs_text)}"
messages = [{"role": "user", "content": prompt}]
# Query all models in parallel
responses = await query_models_parallel(COUNCIL_MODELS, messages)
duration = time.time() - start_time
# Format results
stage1_results = []
successful_models = []
failed_models = []
for model, response in responses.items():
if response is not None: # Only include successful responses
stage1_results.append({
"model": model,
"response": response.get('content', '')
})
successful_models.append(model)
else:
failed_models.append(model)
metadata = {
"duration_seconds": round(duration, 2),
"successful_models": successful_models,
"failed_models": failed_models,
"total_models": len(COUNCIL_MODELS)
}
return stage1_results, metadata
async def stage1_collect_responses_streaming(
user_query: str,
docs_text: Optional[str] = None,
on_response = None
) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
"""
Stage 1: Collect individual responses from all council models, streaming as they complete.
Args:
user_query: The user's question
docs_text: Optional document context
on_response: Optional callback(model, response_dict) called as each response completes
Returns:
Tuple of (results list, metadata dict with timing info)
"""
import asyncio
from .llm_client import query_model, LLM_TIMEOUT_SECONDS, MAX_TOKENS
start_time = time.time()
prompt = f"{user_query}{_format_docs_context(docs_text)}"
messages = [{"role": "user", "content": prompt}]
# Query all models in parallel, but yield results as they complete
async def query_and_notify(model: str):
response = await query_model(model, messages, timeout=LLM_TIMEOUT_SECONDS, max_tokens_override=MAX_TOKENS)
if on_response and response is not None:
result = {"model": model, "response": response.get('content', '')}
await on_response(model, result)
return model, response
tasks = [query_and_notify(model) for model in COUNCIL_MODELS]
responses_dict = {}
# Use as_completed to process results as they finish
for coro in asyncio.as_completed(tasks):
model, response = await coro
responses_dict[model] = response
duration = time.time() - start_time
# Format results
stage1_results = []
successful_models = []
failed_models = []
for model, response in responses_dict.items():
if response is not None: # Only include successful responses
stage1_results.append({
"model": model,
"response": response.get('content', '')
})
successful_models.append(model)
else:
failed_models.append(model)
metadata = {
"duration_seconds": round(duration, 2),
"successful_models": successful_models,
"failed_models": failed_models,
"total_models": len(COUNCIL_MODELS)
}
return stage1_results, metadata
async def stage2_collect_rankings(
user_query: str,
stage1_results: List[Dict[str, Any]],
docs_text: Optional[str] = None,
) -> Tuple[List[Dict[str, Any]], Dict[str, str], Dict[str, Any]]:
"""
Stage 2: Each model ranks the anonymized responses.
Args:
user_query: The original user query
stage1_results: Results from Stage 1
Returns:
Tuple of (rankings list, label_to_model mapping, metadata dict with timing)
"""
start_time = time.time()
# Handle empty stage1_results
if not stage1_results:
return [], {}, {"duration_seconds": 0.0, "successful_models": [], "failed_models": [], "total_models": len(COUNCIL_MODELS)}
# Create anonymized labels for responses (Response A, Response B, etc.)
labels = [chr(65 + i) for i in range(len(stage1_results))] # A, B, C, ...
# Create mapping from label to model name
label_to_model = {
f"Response {label}": result['model']
for label, result in zip(labels, stage1_results)
}
# Build the ranking prompt
responses_text = "\n\n".join([
f"Response {label}:\n{result['response']}"
for label, result in zip(labels, stage1_results)
])
ranking_prompt = f"""You are evaluating different responses to the following question:
Question: {user_query}
{_format_docs_context(docs_text)}
Here are the responses from different models (anonymized):
{responses_text}
Your task:
1. First, evaluate each response individually. For each response, explain what it does well and what it does poorly.
2. Then, at the very end of your response, provide a final ranking.
IMPORTANT: Your final ranking MUST be formatted EXACTLY as follows:
- Start with the line "FINAL RANKING:" (all caps, with colon)
- Then list the responses from best to worst as a numbered list
- Each line should be: number, period, space, then ONLY the response label (e.g., "1. Response A")
- Do not add any other text or explanations in the ranking section
Example of the correct format for your ENTIRE response:
Response A provides good detail on X but misses Y...
Response B is accurate but lacks depth on Z...
Response C offers the most comprehensive answer...
FINAL RANKING:
1. Response C
2. Response A
3. Response B
Now provide your evaluation and ranking:"""
messages = [{"role": "user", "content": ranking_prompt}]
# Get rankings from all council models in parallel
responses = await query_models_parallel(COUNCIL_MODELS, messages)
duration = time.time() - start_time
# Format results
stage2_results = []
successful_models = []
failed_models = []
for model, response in responses.items():
if response is not None:
full_text = response.get('content', '')
parsed = parse_ranking_from_text(full_text)
stage2_results.append({
"model": model,
"ranking": full_text,
"parsed_ranking": parsed
})
successful_models.append(model)
else:
failed_models.append(model)
metadata = {
"duration_seconds": round(duration, 2),
"successful_models": successful_models,
"failed_models": failed_models,
"total_models": len(COUNCIL_MODELS)
}
return stage2_results, label_to_model, metadata
async def stage3_synthesize_final(
user_query: str,
stage1_results: List[Dict[str, Any]],
stage2_results: List[Dict[str, Any]],
docs_text: Optional[str] = None,
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
Stage 3: Chairman synthesizes final response.
Args:
user_query: The original user query
stage1_results: Individual model responses from Stage 1
stage2_results: Rankings from Stage 2
Returns:
Tuple of (result dict with 'model' and 'response' keys, metadata dict with timing)
"""
start_time = time.time()
# Handle empty inputs
if not stage1_results:
duration = time.time() - start_time
return {
"model": CHAIRMAN_MODEL,
"response": "Error: Cannot synthesize final answer - no responses from Stage 1."
}, {
"duration_seconds": round(duration, 2),
"model": CHAIRMAN_MODEL,
"success": False
}
# Build comprehensive context for chairman
# Truncate very long responses to avoid exceeding token/context limits
# More aggressive truncation to keep total prompt under ~2000 tokens (~8000 chars)
MAX_RESPONSE_LENGTH = 2000 # Characters per response
MAX_RANKING_LENGTH = 1000 # Characters per ranking
MAX_DOCS_LENGTH = 2000 # Max characters for docs context
MAX_TOTAL_PROMPT_LENGTH = 8000 # Max total prompt length (safety limit)
def truncate_text(text: str, max_length: int) -> str:
"""Truncate text to max_length, adding ellipsis if truncated."""
if len(text) <= max_length:
return text
return text[:max_length-3] + "..."
# Truncate docs_text if provided
truncated_docs_text = docs_text
if docs_text and len(docs_text) > MAX_DOCS_LENGTH:
truncated_docs_text = truncate_text(docs_text, MAX_DOCS_LENGTH)
stage1_text = "\n\n".join([
f"Model: {result['model']}\nResponse: {truncate_text(result['response'], MAX_RESPONSE_LENGTH)}"
for result in stage1_results
])
stage2_text = "\n\n".join([
f"Model: {result['model']}\nRanking: {truncate_text(result['ranking'], MAX_RANKING_LENGTH)}"
for result in stage2_results
]) if stage2_results else "No rankings available from Stage 2."
chairman_prompt = f"""You are the Chairman of an LLM Council. Multiple AI models have provided responses to a user's question, and then ranked each other's responses.
Original Question: {user_query}
{_format_docs_context(truncated_docs_text)}
STAGE 1 - Individual Responses:
{stage1_text}
STAGE 2 - Peer Rankings:
{stage2_text}
Your task as Chairman is to synthesize all of this information into a single, comprehensive, accurate answer to the user's original question. Consider:
- The individual responses and their insights
- The peer rankings and what they reveal about response quality
- Any patterns of agreement or disagreement
Provide a clear, well-reasoned final answer that represents the council's collective wisdom:"""
# Apply final safety truncation if prompt is still too long
if len(chairman_prompt) > MAX_TOTAL_PROMPT_LENGTH:
# Truncate the prompt itself if it exceeds the limit
chairman_prompt = chairman_prompt[:MAX_TOTAL_PROMPT_LENGTH - 100] + "\n\n[Content truncated due to length limits...]\n\nProvide a clear, well-reasoned final answer:"
messages = [{"role": "user", "content": chairman_prompt}]
# Query the chairman model
# Note: For very long prompts, we might need to truncate or summarize
# For now, we'll try with the full prompt and handle errors gracefully
# Use the default max_tokens (2048) to stay within credit limits
# If you have more credits, you can increase MAX_TOKENS in config.py
response = await query_model(CHAIRMAN_MODEL, messages, timeout=CHAIRMAN_TIMEOUT_SECONDS)
duration = time.time() - start_time
if response is None:
# Try to get more specific error info - check if prompt might be too long
prompt_length = len(chairman_prompt)
estimated_tokens = prompt_length // 4 # Rough estimate: ~4 chars per token
error_msg = (
"Error: Unable to generate final synthesis.\n\n"
"The chairman model failed to respond. Possible causes:\n"
"- Model '{}' not available on the server\n"
"- Server not running or unreachable\n"
"- Network/API errors\n"
"- Prompt too long (estimated ~{} tokens)\n"
"- Server timeout or overloaded\n\n"
"Check the backend terminal logs for the exact error message."
).format(CHAIRMAN_MODEL, estimated_tokens)
return {
"model": CHAIRMAN_MODEL,
"response": error_msg
}, {
"duration_seconds": round(duration, 2),
"model": CHAIRMAN_MODEL,
"success": False
}
return {
"model": CHAIRMAN_MODEL,
"response": response.get('content', '')
}, {
"duration_seconds": round(duration, 2),
"model": CHAIRMAN_MODEL,
"success": True
}
def parse_ranking_from_text(ranking_text: str) -> List[str]:
"""
Parse the FINAL RANKING section from the model's response.
Args:
ranking_text: The full text response from the model
Returns:
List of response labels in ranked order
"""
import re
# Look for "FINAL RANKING:" section
if "FINAL RANKING:" in ranking_text:
# Extract everything after "FINAL RANKING:"
parts = ranking_text.split("FINAL RANKING:")
if len(parts) >= 2:
ranking_section = parts[1]
# Try to extract numbered list format (e.g., "1. Response A")
# This pattern looks for: number, period, optional space, "Response X"
numbered_matches = re.findall(r'\d+\.\s*Response [A-Z]', ranking_section)
if numbered_matches:
# Extract just the "Response X" part
return [re.search(r'Response [A-Z]', m).group() for m in numbered_matches]
# Fallback: Extract all "Response X" patterns in order
matches = re.findall(r'Response [A-Z]', ranking_section)
return matches
# Fallback: try to find any "Response X" patterns in order
matches = re.findall(r'Response [A-Z]', ranking_text)
return matches
def calculate_aggregate_rankings(
stage2_results: List[Dict[str, Any]],
label_to_model: Dict[str, str]
) -> List[Dict[str, Any]]:
"""
Calculate aggregate rankings across all models.
Args:
stage2_results: Rankings from each model
label_to_model: Mapping from anonymous labels to model names
Returns:
List of dicts with model name and average rank, sorted best to worst
"""
from collections import defaultdict
# Track positions for each model
model_positions = defaultdict(list)
for ranking in stage2_results:
ranking_text = ranking['ranking']
# Parse the ranking from the structured format
parsed_ranking = parse_ranking_from_text(ranking_text)
for position, label in enumerate(parsed_ranking, start=1):
if label in label_to_model:
model_name = label_to_model[label]
model_positions[model_name].append(position)
# Calculate average position for each model
aggregate = []
for model, positions in model_positions.items():
if positions:
avg_rank = sum(positions) / len(positions)
aggregate.append({
"model": model,
"average_rank": round(avg_rank, 2),
"rankings_count": len(positions)
})
# Sort by average rank (lower is better)
aggregate.sort(key=lambda x: x['average_rank'])
return aggregate
async def generate_conversation_title(user_query: str) -> str:
"""
Generate a short title for a conversation based on the first user message.
Args:
user_query: The first user message
Returns:
A short title (3-5 words)
"""
title_prompt = f"""Generate a very short title (3-5 words maximum) that summarizes the following question.
The title should be concise and descriptive. Do not use quotes or punctuation in the title.
Question: {user_query}
Title:"""
messages = [{"role": "user", "content": title_prompt}]
# Use chairman model for title generation
# Use configurable timeout (may need longer for local models which load on first request)
response = await query_model(CHAIRMAN_MODEL, messages, timeout=TITLE_GENERATION_TIMEOUT_SECONDS, max_tokens_override=50)
if response is None:
# Fallback to a generic title
return "New Conversation"
title = response.get('content', 'New Conversation').strip()
# Clean up the title - remove quotes, limit length
title = title.strip('"\'')
# Truncate if too long
if len(title) > 50:
title = title[:47] + "..."
return title
async def run_full_council(user_query: str, docs_text: Optional[str] = None) -> Tuple[List, List, Dict, Dict]:
"""
Run the complete 3-stage council process.
Args:
user_query: The user's question
Returns:
Tuple of (stage1_results, stage2_results, stage3_result, metadata)
"""
total_start_time = time.time()
# Stage 1: Collect individual responses
stage1_results, stage1_metadata = await stage1_collect_responses(user_query, docs_text=docs_text)
# If no models responded successfully, return error with helpful message
if not stage1_results:
error_msg = (
"All models failed to respond. This could be due to:\n"
"- Server not running or unreachable\n"
"- Model names not available on the server\n"
"- Network/API errors\n"
"- Server timeout or overloaded\n"
"- Invalid OPENAI_COMPAT_BASE_URL configuration\n\n"
"Check the backend logs for detailed error messages."
)
total_duration = time.time() - total_start_time
return [], [], {
"model": "error",
"response": error_msg
}, {
"label_to_model": {},
"aggregate_rankings": {},
"stage1_metadata": stage1_metadata,
"stage2_metadata": {},
"stage3_metadata": {},
"total_duration_seconds": round(total_duration, 2)
}
# Stage 2: Collect rankings
stage2_results, label_to_model, stage2_metadata = await stage2_collect_rankings(user_query, stage1_results, docs_text=docs_text)
# Calculate aggregate rankings
aggregate_rankings = calculate_aggregate_rankings(stage2_results, label_to_model)
# Stage 3: Synthesize final answer
stage3_result, stage3_metadata = await stage3_synthesize_final(
user_query,
stage1_results,
stage2_results,
docs_text=docs_text,
)
total_duration = time.time() - total_start_time
# Prepare metadata
metadata = {
"label_to_model": label_to_model,
"aggregate_rankings": aggregate_rankings,
"stage1_metadata": stage1_metadata,
"stage2_metadata": stage2_metadata,
"stage3_metadata": stage3_metadata,
"total_duration_seconds": round(total_duration, 2)
}
return stage1_results, stage2_results, stage3_result, metadata

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"""Helpers to load and format uploaded markdown docs as prompt context."""
from __future__ import annotations
import re
from typing import Optional, List
from . import documents
def _normalize_filename_for_matching(filename: str) -> str:
"""Normalize filename for matching @filename references."""
# Convert to lowercase, replace spaces/underscores/hyphens with single underscore
normalized = filename.lower()
normalized = re.sub(r'[_\s\-]+', '_', normalized)
# Remove .md extension for matching
normalized = normalized.replace('.md', '')
return normalized
def _extract_filename_references(text: str) -> List[str]:
"""Extract @filename references from text."""
# Match @filename patterns (with or without .md extension)
pattern = r'@([a-zA-Z0-9_\s\-\+\.]+)'
matches = re.findall(pattern, text)
# Normalize each match
return [_normalize_filename_for_matching(m) for m in matches]
def _extract_numeric_references(text: str) -> List[int]:
"""Extract numeric document references like @1, @2, @3 from text."""
# Match @ followed by digits
pattern = r'@(\d+)'
matches = re.findall(pattern, text)
# Convert to integers (1-indexed, will be converted to 0-indexed when used)
return [int(m) for m in matches]
def build_docs_context(
conversation_id: str,
user_query: Optional[str] = None,
*,
max_chars: int = 8000,
max_docs: int = 5
) -> Optional[str]:
"""
Return a single markdown string containing (truncated) docs for a conversation.
If user_query is provided and contains references:
- @1, @2, @3 etc. (numeric): Include documents by their numbered position (1-indexed)
- @filename (text): Include documents whose filenames match (fuzzy matching)
- If both are present, numeric references take precedence
Otherwise, include all documents up to max_docs.
"""
all_metas = documents.list_documents(conversation_id)
if not all_metas:
return None
# Check for numeric references first (e.g., @1, @2, @3)
if user_query:
numeric_refs = _extract_numeric_references(user_query)
if numeric_refs:
# Convert 1-indexed to 0-indexed and filter
filtered_metas = []
for num in numeric_refs:
idx = num - 1 # Convert to 0-indexed
if 0 <= idx < len(all_metas):
filtered_metas.append(all_metas[idx])
if filtered_metas:
all_metas = filtered_metas
else:
# If no numeric refs, check for filename references
refs = _extract_filename_references(user_query)
if refs:
filtered_metas = []
for meta in all_metas:
normalized = _normalize_filename_for_matching(meta.filename)
# Check if any reference matches this filename
if any(ref in normalized or normalized in ref for ref in refs):
filtered_metas.append(meta)
if filtered_metas:
all_metas = filtered_metas
# Limit to max_docs
metas = all_metas[:max_docs]
if not metas:
return None
chunks = []
remaining = max_chars
for meta in metas:
if remaining <= 0:
break
text = documents.read_document_text(conversation_id, meta.id)
header = f"\n\n---\nDOC: {meta.filename} ({meta.bytes} bytes)\n---\n"
body = text
if len(header) >= remaining:
break
remaining -= len(header)
if len(body) > remaining:
body = body[: max(0, remaining - 3)] + "..."
remaining -= len(body)
chunks.append(header + body)
return "".join(chunks).strip() if chunks else None

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"""Markdown document storage for conversations.
Stores uploaded .md files on disk under data/docs/<conversation_id>/.
"""
from __future__ import annotations
import os
import re
import uuid
from dataclasses import dataclass
from pathlib import Path
from typing import List
from .config import DOCS_DIR, MAX_DOC_BYTES
_SAFE_NAME_RE = re.compile(r"[^a-zA-Z0-9._ -]+")
def _safe_filename(name: str) -> str:
name = name.strip().replace("\\", "/").split("/")[-1] # drop any path
name = _SAFE_NAME_RE.sub("_", name)
name = name.strip(" .")
if not name:
name = "document.md"
if not name.lower().endswith(".md"):
name = f"{name}.md"
return name
def _conversation_dir(conversation_id: str) -> Path:
base = Path(DOCS_DIR)
return base / conversation_id
def ensure_docs_dir(conversation_id: str) -> Path:
d = _conversation_dir(conversation_id)
d.mkdir(parents=True, exist_ok=True)
return d
@dataclass(frozen=True)
class DocumentMeta:
id: str
filename: str
bytes: int
def save_markdown_document(conversation_id: str, filename: str, content: bytes) -> DocumentMeta:
if len(content) > MAX_DOC_BYTES:
raise ValueError(f"Document too large. Max {MAX_DOC_BYTES} bytes.")
safe_name = _safe_filename(filename)
doc_id = str(uuid.uuid4())
d = ensure_docs_dir(conversation_id)
path = d / f"{doc_id}__{safe_name}"
path.write_bytes(content)
return DocumentMeta(id=doc_id, filename=safe_name, bytes=len(content))
def list_documents(conversation_id: str) -> List[DocumentMeta]:
d = _conversation_dir(conversation_id)
if not d.exists():
return []
out: List[DocumentMeta] = []
for p in sorted(d.iterdir()):
if not p.is_file():
continue
if "__" not in p.name:
continue
doc_id, fname = p.name.split("__", 1)
out.append(DocumentMeta(id=doc_id, filename=fname, bytes=p.stat().st_size))
return out
def read_document_text(conversation_id: str, doc_id: str) -> str:
d = _conversation_dir(conversation_id)
if not d.exists():
raise FileNotFoundError("Conversation docs not found")
matches = [p for p in d.iterdir() if p.is_file() and p.name.startswith(f"{doc_id}__")]
if not matches:
raise FileNotFoundError("Document not found")
raw = matches[0].read_bytes()
# Best-effort UTF-8; replace invalid sequences
return raw.decode("utf-8", errors="replace")
def delete_document(conversation_id: str, doc_id: str) -> None:
d = _conversation_dir(conversation_id)
if not d.exists():
raise FileNotFoundError("Conversation docs not found")
matches = [p for p in d.iterdir() if p.is_file() and p.name.startswith(f"{doc_id}__")]
if not matches:
raise FileNotFoundError("Document not found")
matches[0].unlink()

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"""Unified LLM client.
This module routes LLM requests to OpenAI-compatible servers (Ollama, vLLM, TGI, etc.).
The base URL is determined by:
- If USE_LOCAL_OLLAMA=true: uses http://localhost:11434
- Else if OPENAI_COMPAT_BASE_URL is set: uses that URL
- Else: raises an error (base URL must be configured)
"""
from __future__ import annotations
import os
from typing import Any, Dict, List, Optional
from .config import MAX_TOKENS, OPENAI_COMPAT_BASE_URL, LLM_TIMEOUT_SECONDS, DEBUG
def _get_provider_name() -> str:
"""Returns the provider name (always 'openai_compat' now)."""
return "openai_compat"
def _get_max_concurrency() -> int:
"""
Maximum number of in-flight model requests when calling query_models_parallel.
- If LLM_MAX_CONCURRENCY is unset/empty/invalid: unlimited (0)
- If set to 1: strictly sequential
- If set to N>1: at most N in flight
"""
raw = (os.getenv("LLM_MAX_CONCURRENCY") or "").strip()
if not raw:
return 0
try:
v = int(raw)
except ValueError:
return 0
return max(0, v)
def get_provider_info() -> Dict[str, Any]:
"""Get information about the configured provider."""
from .config import OPENAI_COMPAT_BASE_URL
return {
"provider": "openai_compat",
"base_url": OPENAI_COMPAT_BASE_URL
}
async def list_models() -> Optional[List[str]]:
"""List available models from the OpenAI-compatible server."""
from .openai_compat import list_models as _list
return await _list()
async def query_model(
model: str,
messages: List[Dict[str, str]],
timeout: Optional[float] = None,
max_tokens_override: Optional[int] = None,
) -> Optional[Dict[str, Any]]:
"""Query a model via OpenAI-compatible API."""
from .openai_compat import query_model as _query
max_tokens = max_tokens_override if max_tokens_override is not None else MAX_TOKENS
resolved_timeout = timeout if timeout is not None else LLM_TIMEOUT_SECONDS
return await _query(
model,
messages,
max_tokens=max_tokens,
timeout=resolved_timeout,
)
async def query_models_parallel(
models: List[str],
messages: List[Dict[str, str]],
timeout: Optional[float] = None,
max_tokens_override: Optional[int] = None,
) -> Dict[str, Optional[Dict[str, Any]]]:
import asyncio
resolved_timeout = timeout if timeout is not None else LLM_TIMEOUT_SECONDS
limit = _get_max_concurrency()
# If limit is 1, run completely sequentially (one at a time, wait for each to finish)
if limit == 1:
results = {}
for model in models:
if DEBUG:
print(f"[DEBUG] Running model '{model}' sequentially (concurrency=1)")
results[model] = await query_model(
model,
messages,
timeout=resolved_timeout,
max_tokens_override=max_tokens_override,
)
return results
# If limit <= 0 or >= len(models), run all in parallel (no limit)
if limit <= 0 or limit >= len(models):
tasks = [
query_model(
model,
messages,
timeout=resolved_timeout,
max_tokens_override=max_tokens_override,
)
for model in models
]
responses = await asyncio.gather(*tasks)
return {model: response for model, response in zip(models, responses)}
# Otherwise, use semaphore to limit concurrency (2, 3, etc.)
sem = asyncio.Semaphore(limit)
async def _run_one(model: str) -> Optional[Dict[str, Any]]:
async with sem:
return await query_model(
model,
messages,
timeout=resolved_timeout,
max_tokens_override=max_tokens_override,
)
tasks = [_run_one(model) for model in models]
responses = await asyncio.gather(*tasks)
return {model: response for model, response in zip(models, responses)}

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"""FastAPI backend for LLM Council."""
from fastapi import FastAPI, HTTPException, UploadFile, File, Query
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse, Response
from pydantic import BaseModel
from typing import List, Dict, Any
import uuid
import json
import asyncio
import time
from datetime import datetime
from . import storage
from . import documents
from .config import MAX_DOC_PREVIEW_CHARS, COUNCIL_MODELS
from .docs_context import build_docs_context
from .llm_client import get_provider_info, list_models as llm_list_models, query_model, LLM_TIMEOUT_SECONDS, MAX_TOKENS
from .council import run_full_council, generate_conversation_title, stage1_collect_responses, stage2_collect_rankings, stage3_synthesize_final, calculate_aggregate_rankings, _format_docs_context
app = FastAPI(title="LLM Council API")
# Enable CORS for local development
app.add_middleware(
CORSMiddleware,
allow_origins=["http://localhost:5173", "http://localhost:5174", "http://localhost:3000"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
class CreateConversationRequest(BaseModel):
"""Request to create a new conversation."""
pass
class SendMessageRequest(BaseModel):
"""Request to send a message in a conversation."""
content: str
class ConversationMetadata(BaseModel):
"""Conversation metadata for list view."""
id: str
created_at: str
title: str
message_count: int
class Conversation(BaseModel):
"""Full conversation with all messages."""
id: str
created_at: str
title: str
messages: List[Dict[str, Any]]
@app.get("/")
async def root():
"""Health check endpoint."""
return {"status": "ok", "service": "LLM Council API"}
@app.get("/api/llm/status")
async def llm_status(probe: bool = Query(False, description="If true, query the provider for available models")):
"""
Returns current LLM provider configuration and (optionally) probes the provider.
"""
info = get_provider_info()
if probe:
info["remote_models"] = await llm_list_models()
return info
@app.get("/api/conversations", response_model=List[ConversationMetadata])
async def list_conversations():
"""List all conversations (metadata only)."""
return storage.list_conversations()
@app.post("/api/conversations", response_model=Conversation)
async def create_conversation(request: CreateConversationRequest):
"""Create a new conversation."""
conversation_id = str(uuid.uuid4())
conversation = storage.create_conversation(conversation_id)
return conversation
@app.get("/api/conversations/{conversation_id}", response_model=Conversation)
async def get_conversation(conversation_id: str):
"""Get a specific conversation with all its messages."""
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
return conversation
@app.delete("/api/conversations/{conversation_id}")
async def delete_conversation(conversation_id: str):
"""Delete a conversation and its associated documents."""
try:
storage.delete_conversation(conversation_id)
except ValueError as e:
raise HTTPException(status_code=404, detail=str(e))
return {"ok": True}
@app.get("/api/conversations/{conversation_id}/documents")
async def list_conversation_documents(conversation_id: str):
"""List uploaded markdown documents for a conversation."""
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
docs = documents.list_documents(conversation_id)
return [{"id": d.id, "filename": d.filename, "bytes": d.bytes} for d in docs]
@app.post("/api/conversations/{conversation_id}/documents")
async def upload_conversation_document(
conversation_id: str,
files: List[UploadFile] = File(default=[]),
file: UploadFile = File(default=None),
):
"""
Upload one or more markdown documents (.md) for a conversation.
Backwards compatible:
- old clients send a single "file" field
- new clients can send multiple "files" fields
"""
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
incoming: List[UploadFile] = []
if file is not None:
incoming.append(file)
if files:
incoming.extend(files)
if not incoming:
raise HTTPException(status_code=400, detail="No files uploaded")
uploaded = []
for f in incoming:
filename = f.filename or "document.md"
if not filename.lower().endswith(".md"):
raise HTTPException(status_code=400, detail="Only .md files are supported")
content = await f.read()
try:
meta = documents.save_markdown_document(conversation_id, filename, content)
except ValueError as e:
raise HTTPException(status_code=400, detail=str(e))
uploaded.append({"id": meta.id, "filename": meta.filename, "bytes": meta.bytes})
# Back-compat: if single file uploaded, return the single object shape.
if len(uploaded) == 1:
return uploaded[0]
return {"uploaded": uploaded}
@app.get("/api/conversations/{conversation_id}/documents/{doc_id}")
async def get_conversation_document(conversation_id: str, doc_id: str):
"""Fetch a markdown document's text (truncated for safety)."""
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
try:
text = documents.read_document_text(conversation_id, doc_id)
except FileNotFoundError:
raise HTTPException(status_code=404, detail="Document not found")
if len(text) > MAX_DOC_PREVIEW_CHARS:
text = text[: MAX_DOC_PREVIEW_CHARS - 3] + "..."
return {"id": doc_id, "content": text}
@app.delete("/api/conversations/{conversation_id}/documents/{doc_id}")
async def delete_conversation_document(conversation_id: str, doc_id: str):
"""Delete a previously uploaded document."""
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
try:
documents.delete_document(conversation_id, doc_id)
except FileNotFoundError:
raise HTTPException(status_code=404, detail="Document not found")
return {"ok": True}
@app.patch("/api/conversations/{conversation_id}/title")
async def update_conversation_title_endpoint(conversation_id: str, request: dict):
"""Update the title of a conversation."""
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
new_title = request.get('title', '').strip()
if not new_title:
raise HTTPException(status_code=400, detail="Title cannot be empty")
storage.update_conversation_title(conversation_id, new_title)
return {"ok": True, "title": new_title}
@app.get("/api/conversations/search")
async def search_conversations(q: str = ""):
"""Search conversations by title and content."""
if not q or len(q.strip()) < 2:
return []
query = q.strip().lower()
all_conversations = storage.list_conversations()
results = []
for conv_meta in all_conversations:
# Search in title
title_match = query in (conv_meta.get('title', '') or '').lower()
# Search in content
conv = storage.get_conversation(conv_meta['id'])
content_match = False
if conv:
for msg in conv.get('messages', []):
if query in msg.get('content', '').lower():
content_match = True
break
if title_match or content_match:
results.append(conv_meta)
return results
@app.post("/api/conversations/{conversation_id}/message")
async def send_message(conversation_id: str, request: SendMessageRequest):
"""
Send a message and run the 3-stage council process.
Returns the complete response with all stages.
"""
# Check if conversation exists
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
# Check if this is the first message
is_first_message = len(conversation["messages"]) == 0
# Add user message
storage.add_user_message(conversation_id, request.content)
# If this is the first message, generate a title
if is_first_message:
title = await generate_conversation_title(request.content)
storage.update_conversation_title(conversation_id, title)
# Run the 3-stage council process
docs_text = build_docs_context(conversation_id, user_query=request.content)
stage1_results, stage2_results, stage3_result, metadata = await run_full_council(
request.content,
docs_text=docs_text,
)
# Add assistant message with all stages
storage.add_assistant_message(
conversation_id,
stage1_results,
stage2_results,
stage3_result,
metadata
)
# Return the complete response with metadata
return {
"stage1": stage1_results,
"stage2": stage2_results,
"stage3": stage3_result,
"metadata": metadata
}
@app.post("/api/conversations/{conversation_id}/message/stream")
async def send_message_stream(conversation_id: str, request: SendMessageRequest):
"""
Send a message and stream the 3-stage council process.
Returns Server-Sent Events as each stage completes.
"""
# Check if conversation exists
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
# Check if this is the first message
is_first_message = len(conversation["messages"]) == 0
async def event_generator():
try:
# Add user message
storage.add_user_message(conversation_id, request.content)
# Start title generation in parallel (don't await yet)
title_task = None
if is_first_message:
title_task = asyncio.create_task(generate_conversation_title(request.content))
# Load docs context once per request
docs_text = build_docs_context(conversation_id, user_query=request.content)
# Stage 1: Collect responses - stream individual responses as they complete
yield f"data: {json.dumps({'type': 'stage1_start'})}\n\n"
# Stream responses as they complete
from .config import COUNCIL_MODELS, OPENAI_COMPAT_BASE_URL, DEBUG
from .llm_client import query_model, LLM_TIMEOUT_SECONDS, MAX_TOKENS
from .council import _format_docs_context
if DEBUG:
print(f"[DEBUG] Stage 1: Querying {len(COUNCIL_MODELS)} models: {COUNCIL_MODELS}")
print(f"[DEBUG] Using base URL: {OPENAI_COMPAT_BASE_URL}")
start_time = time.time()
prompt = f"{request.content}{_format_docs_context(docs_text)}"
messages = [{"role": "user", "content": prompt}]
stage1_results = []
successful_models = []
failed_models = []
response_queue = asyncio.Queue()
async def process_model(model: str):
try:
if DEBUG:
print(f"[DEBUG] Processing model: {model}")
response = await query_model(model, messages, timeout=LLM_TIMEOUT_SECONDS, max_tokens_override=MAX_TOKENS)
if response is not None:
result = {"model": model, "response": response.get('content', '')}
await response_queue.put(('success', model, result))
if DEBUG:
print(f"[DEBUG] Model {model} succeeded")
else:
await response_queue.put(('failed', model, None))
if DEBUG:
print(f"[DEBUG] Model {model} failed (returned None)")
except Exception as e:
await response_queue.put(('failed', model, None))
if DEBUG:
print(f"[DEBUG] Model {model} exception: {e}")
# Create tasks
tasks = [asyncio.create_task(process_model(model)) for model in COUNCIL_MODELS]
# Process responses as they arrive
completed = 0
while completed < len(COUNCIL_MODELS):
status, model, result = await response_queue.get()
if status == 'success':
stage1_results.append(result)
successful_models.append(model)
# Stream this response immediately
yield f"data: {json.dumps({'type': 'stage1_response', 'model': model, 'response': result})}\n\n"
else:
failed_models.append(model)
# Stream failure notification
yield f"data: {json.dumps({'type': 'stage1_response_failed', 'model': model})}\n\n"
completed += 1
# Wait for all tasks to complete
await asyncio.gather(*tasks, return_exceptions=True)
duration = time.time() - start_time
stage1_metadata = {
"duration_seconds": round(duration, 2),
"successful_models": successful_models,
"failed_models": failed_models,
"total_models": len(COUNCIL_MODELS)
}
yield f"data: {json.dumps({'type': 'stage1_complete', 'data': stage1_results, 'metadata': stage1_metadata})}\n\n"
# Stage 2: Collect rankings
yield f"data: {json.dumps({'type': 'stage2_start'})}\n\n"
stage2_results, label_to_model, stage2_metadata = await stage2_collect_rankings(request.content, stage1_results, docs_text=docs_text)
aggregate_rankings = calculate_aggregate_rankings(stage2_results, label_to_model)
yield f"data: {json.dumps({'type': 'stage2_complete', 'data': stage2_results, 'metadata': {'label_to_model': label_to_model, 'aggregate_rankings': aggregate_rankings, 'stage2_metadata': stage2_metadata}})}\n\n"
# Stage 3: Synthesize final answer
yield f"data: {json.dumps({'type': 'stage3_start'})}\n\n"
stage3_result, stage3_metadata = await stage3_synthesize_final(request.content, stage1_results, stage2_results, docs_text=docs_text)
yield f"data: {json.dumps({'type': 'stage3_complete', 'data': stage3_result, 'metadata': stage3_metadata})}\n\n"
# Wait for title generation if it was started
if title_task:
title = await title_task
storage.update_conversation_title(conversation_id, title)
yield f"data: {json.dumps({'type': 'title_complete', 'data': {'title': title}})}\n\n"
# Prepare metadata
metadata = {
"label_to_model": label_to_model,
"aggregate_rankings": aggregate_rankings,
"stage1_metadata": stage1_metadata,
"stage2_metadata": stage2_metadata,
"stage3_metadata": stage3_metadata
}
# Save complete assistant message
storage.add_assistant_message(
conversation_id,
stage1_results,
stage2_results,
stage3_result,
metadata
)
# Send completion event
yield f"data: {json.dumps({'type': 'complete'})}\n\n"
except Exception as e:
# Send error event with details
import traceback
from .config import DEBUG
error_msg = str(e)
if DEBUG:
error_msg += f"\n{traceback.format_exc()}"
print(f"[ERROR] Stream error: {error_msg}")
yield f"data: {json.dumps({'type': 'error', 'message': error_msg})}\n\n"
return StreamingResponse(
event_generator(),
media_type="text/event-stream",
headers={
"Cache-Control": "no-cache",
"Connection": "keep-alive",
}
)
@app.get("/api/conversations/{conversation_id}/export")
async def export_conversation_report(conversation_id: str):
"""Export conversation as a markdown report file."""
conversation = storage.get_conversation(conversation_id)
if conversation is None:
raise HTTPException(status_code=404, detail="Conversation not found")
# Get document list to map @1, @2, etc. to filenames
from . import documents
doc_list = documents.list_documents(conversation_id)
doc_map = {} # Maps @1 -> filename, @2 -> filename, etc.
for idx, doc in enumerate(doc_list, 1):
doc_map[f"@{idx}"] = doc.filename
doc_map[f"@ {idx}"] = doc.filename # Also handle @ 1 (with space)
def replace_doc_references(text: str) -> str:
"""Replace @1, @2, etc. with actual filenames."""
import re
# Replace @1, @2, @3, etc. with filenames
for ref, filename in doc_map.items():
# Match @1, @2, etc. (with optional space after @)
pattern = re.escape(ref)
text = re.sub(pattern, filename, text)
return text
# Generate markdown report
lines = []
lines.append(f"# {conversation.get('title', 'Conversation')}\n")
# Add metadata
created_at = conversation.get('created_at', '')
if created_at:
try:
dt = datetime.fromisoformat(created_at.replace('Z', '+00:00'))
lines.append(f"**Created:** {dt.strftime('%Y-%m-%d %H:%M:%S UTC')}\n")
except:
lines.append(f"**Created:** {created_at}\n")
lines.append(f"**Conversation ID:** {conversation_id}\n")
lines.append("\n---\n\n")
# Add messages
for msg_idx, msg in enumerate(conversation.get('messages', []), 1):
if msg['role'] == 'user':
lines.append(f"## User Message {msg_idx}\n\n")
content = replace_doc_references(msg['content'])
lines.append(f"{content}\n\n")
lines.append("---\n\n")
elif msg['role'] == 'assistant':
lines.append(f"## LLM Council Response {msg_idx}\n\n")
metadata = msg.get('metadata', {})
# Stage 1
if msg.get('stage1'):
stage1_meta = metadata.get('stage1_metadata', {})
duration = stage1_meta.get('duration_seconds', 0)
successful = len(stage1_meta.get('successful_models', []))
total = stage1_meta.get('total_models', 0)
lines.append("### Stage 1: Individual Responses\n\n")
if duration:
lines.append(f"*Duration: {duration}s | Successful: {successful}/{total} models*\n\n")
for response in msg['stage1']:
lines.append(f"#### {response['model']}\n\n")
content = replace_doc_references(response['response'])
lines.append(f"{content}\n\n")
lines.append("\n---\n\n")
# Stage 2
if msg.get('stage2'):
stage2_meta = metadata.get('stage2_metadata', {})
duration = stage2_meta.get('duration_seconds', 0)
successful = len(stage2_meta.get('successful_models', []))
total = stage2_meta.get('total_models', 0)
lines.append("### Stage 2: Peer Rankings\n\n")
if duration:
lines.append(f"*Duration: {duration}s | Successful: {successful}/{total} models*\n\n")
for ranking in msg['stage2']:
lines.append(f"#### {ranking['model']}\n\n")
content = replace_doc_references(ranking['ranking'])
lines.append(f"{content}\n\n")
# Add aggregate rankings if available
agg_rankings = metadata.get('aggregate_rankings', [])
if agg_rankings:
lines.append("#### Aggregate Rankings\n\n")
for item in agg_rankings:
lines.append(f"- **{item['model']}**: Average rank {item['average_rank']:.2f}\n")
lines.append("\n")
lines.append("\n---\n\n")
# Stage 3
if msg.get('stage3'):
stage3_meta = metadata.get('stage3_metadata', {})
duration = stage3_meta.get('duration_seconds', 0)
model = stage3_meta.get('model', msg['stage3'].get('model', 'Unknown'))
lines.append("### Stage 3: Final Synthesis\n\n")
if duration:
lines.append(f"*Duration: {duration}s | Model: {model}*\n\n")
content = replace_doc_references(msg['stage3'].get('response', ''))
lines.append(f"{content}\n\n")
# Total duration
total_duration = metadata.get('total_duration_seconds')
if total_duration:
lines.append(f"**Total processing time:** {total_duration}s\n\n")
lines.append("---\n\n")
# Convert to string
content = "".join(lines)
# Generate filename
title = conversation.get('title', 'conversation')
# Sanitize filename
safe_title = "".join(c if c.isalnum() or c in (' ', '-', '_') else '_' for c in title)
safe_title = safe_title[:50].strip() # Limit length
filename = f"{safe_title}_{conversation_id[:8]}.md"
return Response(
content=content,
media_type="text/markdown",
headers={
"Content-Disposition": f'attachment; filename="{filename}"'
}
)
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8001)

253
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"""OpenAI-compatible API client (for Ollama / vLLM / TGI / OpenAI-style servers).
This lets LLM Council talk to any OpenAI-compatible server (local Ollama,
remote Ollama, vLLM, TGI, etc.).
"""
from __future__ import annotations
import asyncio
import os
from typing import Any, Dict, List, Optional
import httpx
from .config import (
OPENAI_COMPAT_BASE_URL,
OPENAI_COMPAT_RETRIES,
OPENAI_COMPAT_RETRY_BACKOFF_SECONDS,
OPENAI_COMPAT_TIMEOUT_SECONDS,
OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS,
OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS,
OPENAI_COMPAT_POOL_TIMEOUT_SECONDS,
DEBUG,
)
def _resolve_chat_completions_url(base_url: str) -> str:
"""
Accepts either:
- http://host:8000 -> http://host:8000/v1/chat/completions
- http://host:8000/v1 -> http://host:8000/v1/chat/completions
- http://host:8000/v1/ -> http://host:8000/v1/chat/completions
"""
base = base_url.rstrip("/")
if base.endswith("/v1"):
return f"{base}/chat/completions"
if "/v1/" in f"{base}/":
# Already has /v1 somewhere; assume caller gave full root including /v1
return f"{base}/chat/completions"
return f"{base}/v1/chat/completions"
def _resolve_models_url(base_url: str) -> str:
base = base_url.rstrip("/")
if base.endswith("/v1"):
return f"{base}/models"
if "/v1/" in f"{base}/":
return f"{base}/models"
return f"{base}/v1/models"
def _resolve_ollama_tags_url(base_url: str) -> str:
"""Resolve Ollama's native /api/tags endpoint URL."""
base = base_url.rstrip("/")
return f"{base}/api/tags"
def _should_retry(status_code: int) -> bool:
return status_code in {408, 409, 425, 429, 500, 502, 503, 504}
async def query_model(
model: str,
messages: List[Dict[str, str]],
*,
base_url: Optional[str] = None,
api_key: Optional[str] = None,
max_tokens: int = 2048,
timeout: Optional[float] = None,
client: Optional[httpx.AsyncClient] = None,
) -> Optional[Dict[str, Any]]:
"""Query a model via an OpenAI-compatible chat completions endpoint."""
resolved_base_url = base_url or OPENAI_COMPAT_BASE_URL
if not resolved_base_url:
print("Error querying OpenAI-compatible provider: OPENAI_COMPAT_BASE_URL not set")
return None
resolved_api_key = api_key if api_key is not None else os.getenv("OPENAI_COMPAT_API_KEY")
resolved_timeout = OPENAI_COMPAT_TIMEOUT_SECONDS if timeout is None else timeout
retries = OPENAI_COMPAT_RETRIES
backoff = OPENAI_COMPAT_RETRY_BACKOFF_SECONDS
url = _resolve_chat_completions_url(resolved_base_url)
headers = {"Content-Type": "application/json"}
if resolved_api_key:
headers["Authorization"] = f"Bearer {resolved_api_key}"
payload: Dict[str, Any] = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
}
if DEBUG:
print(f"[DEBUG] Querying model '{model}' at {url} (timeout={resolved_timeout}s, max_tokens={max_tokens})")
close_client = False
try:
if client is None:
# Use explicit Timeout object to ensure read timeout is set correctly
# For LLM requests, we need a long read timeout since generation can take time
timeout_config = httpx.Timeout(
connect=OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS,
read=resolved_timeout, # Read timeout: use the configured timeout
write=OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS,
pool=OPENAI_COMPAT_POOL_TIMEOUT_SECONDS
)
client = httpx.AsyncClient(timeout=timeout_config)
close_client = True
attempt = 0
while True:
if DEBUG:
print(f"[DEBUG] Attempt {attempt + 1}/{retries + 1}: POST {url}")
resp = await client.post(url, headers=headers, json=payload)
if resp.status_code != 200:
# Preserve server-provided error text for debugging.
try:
err_json = resp.json()
err_msg = err_json.get("error", {}).get("message", resp.text)
except Exception:
err_msg = resp.text
if attempt < retries and _should_retry(resp.status_code):
await asyncio.sleep(backoff * (2**attempt))
attempt += 1
continue
print(f"Error querying model {model} (HTTP {resp.status_code}): {err_msg}")
return None
data = resp.json()
msg = data["choices"][0]["message"]
if DEBUG:
print(f"[DEBUG] Model '{model}' responded successfully")
return {
"content": msg.get("content"),
"reasoning_details": msg.get("reasoning_details"),
}
except httpx.TimeoutException as e:
print(f"[ERROR] Model '{model}' timeout after {resolved_timeout}s at {url}")
print(
f"[ERROR] This can mean the model is loading / slow, OR that the server/port is unreachable.\n"
f"[ERROR] Check connectivity: curl {resolved_base_url}/api/tags"
)
return None
except httpx.ConnectError as e:
print(f"[ERROR] Cannot connect to {url}: {e}")
print(f"[ERROR] Is Ollama running? Check: curl {resolved_base_url}/api/tags")
return None
except Exception as e:
print(f"[ERROR] Unexpected error querying model '{model}' at {url}: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
return None
finally:
if close_client and client is not None:
await client.aclose()
async def list_models(
*,
base_url: Optional[str] = None,
api_key: Optional[str] = None,
timeout: Optional[float] = None,
client: Optional[httpx.AsyncClient] = None,
) -> Optional[List[str]]:
"""Return model IDs from an OpenAI-compatible server (/v1/models)."""
resolved_base_url = base_url or OPENAI_COMPAT_BASE_URL
if not resolved_base_url:
return None
resolved_api_key = api_key if api_key is not None else os.getenv("OPENAI_COMPAT_API_KEY")
resolved_timeout = OPENAI_COMPAT_TIMEOUT_SECONDS if timeout is None else timeout
retries = OPENAI_COMPAT_RETRIES
backoff = OPENAI_COMPAT_RETRY_BACKOFF_SECONDS
# Try OpenAI-compatible endpoint first
url = _resolve_models_url(resolved_base_url)
headers = {"Content-Type": "application/json"}
if resolved_api_key:
headers["Authorization"] = f"Bearer {resolved_api_key}"
close_client = False
try:
if client is None:
# Use explicit Timeout object for list_models (faster operation)
timeout_config = httpx.Timeout(
connect=OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS,
read=resolved_timeout,
write=OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS,
pool=OPENAI_COMPAT_POOL_TIMEOUT_SECONDS
)
client = httpx.AsyncClient(timeout=timeout_config)
close_client = True
attempt = 0
while True:
resp = await client.get(url, headers=headers)
if resp.status_code == 200:
data = resp.json()
# Try OpenAI-compatible format first
items = data.get("data", [])
if items:
ids: List[str] = []
for it in items:
mid = it.get("id")
if mid:
ids.append(mid)
return ids
# Fallback: check if it's already in Ollama format
items = data.get("models", [])
if items:
ids: List[str] = []
for it in items:
mid = it.get("name") or it.get("model")
if mid:
ids.append(mid)
return ids
return []
# If /v1/models fails, try Ollama's native /api/tags endpoint
if resp.status_code == 404 and attempt == 0:
ollama_url = _resolve_ollama_tags_url(resolved_base_url)
if DEBUG:
print(f"[DEBUG] /v1/models not found, trying Ollama native API: {ollama_url}")
resp = await client.get(ollama_url, headers=headers)
if resp.status_code == 200:
data = resp.json()
items = data.get("models", [])
if items:
ids: List[str] = []
for it in items:
mid = it.get("name") or it.get("model")
if mid:
ids.append(mid)
return ids
if attempt < retries and _should_retry(resp.status_code):
await asyncio.sleep(backoff * (2**attempt))
attempt += 1
continue
return None
except Exception as e:
if DEBUG:
msg = str(e) if str(e) else "(no message)"
print(f"[DEBUG] Error listing models: {type(e).__name__}: {msg}")
return None
finally:
if close_client and client is not None:
await client.aclose()

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"""JSON-based storage for conversations."""
import json
import os
from datetime import datetime
from typing import List, Dict, Any, Optional
from pathlib import Path
from .config import DATA_DIR
def ensure_data_dir():
"""Ensure the data directory exists."""
Path(DATA_DIR).mkdir(parents=True, exist_ok=True)
def get_conversation_path(conversation_id: str) -> str:
"""Get the file path for a conversation."""
return os.path.join(DATA_DIR, f"{conversation_id}.json")
def create_conversation(conversation_id: str) -> Dict[str, Any]:
"""
Create a new conversation.
Args:
conversation_id: Unique identifier for the conversation
Returns:
New conversation dict
"""
ensure_data_dir()
conversation = {
"id": conversation_id,
"created_at": datetime.utcnow().isoformat(),
"title": "New Conversation",
"messages": []
}
# Save to file
path = get_conversation_path(conversation_id)
with open(path, 'w') as f:
json.dump(conversation, f, indent=2)
return conversation
def get_conversation(conversation_id: str) -> Optional[Dict[str, Any]]:
"""
Load a conversation from storage.
Args:
conversation_id: Unique identifier for the conversation
Returns:
Conversation dict or None if not found
"""
path = get_conversation_path(conversation_id)
if not os.path.exists(path):
return None
with open(path, 'r') as f:
return json.load(f)
def save_conversation(conversation: Dict[str, Any]):
"""
Save a conversation to storage.
Args:
conversation: Conversation dict to save
"""
ensure_data_dir()
path = get_conversation_path(conversation['id'])
with open(path, 'w') as f:
json.dump(conversation, f, indent=2)
def list_conversations(include_archived: bool = False) -> List[Dict[str, Any]]:
"""
List all conversations (metadata only).
Args:
include_archived: If True, include archived conversations
Returns:
List of conversation metadata dicts
"""
ensure_data_dir()
conversations = []
for filename in os.listdir(DATA_DIR):
if filename.endswith('.json'):
path = os.path.join(DATA_DIR, filename)
with open(path, 'r') as f:
data = json.load(f)
# Return metadata only
conversations.append({
"id": data["id"],
"created_at": data["created_at"],
"title": data.get("title", "New Conversation"),
"message_count": len(data["messages"])
})
# Sort by creation time, newest first
conversations.sort(key=lambda x: x["created_at"], reverse=True)
return conversations
def add_user_message(conversation_id: str, content: str):
"""
Add a user message to a conversation.
Args:
conversation_id: Conversation identifier
content: User message content
"""
conversation = get_conversation(conversation_id)
if conversation is None:
raise ValueError(f"Conversation {conversation_id} not found")
conversation["messages"].append({
"role": "user",
"content": content
})
save_conversation(conversation)
def add_assistant_message(
conversation_id: str,
stage1: List[Dict[str, Any]],
stage2: List[Dict[str, Any]],
stage3: Dict[str, Any],
metadata: Optional[Dict[str, Any]] = None
):
"""
Add an assistant message with all 3 stages to a conversation.
Args:
conversation_id: Conversation identifier
stage1: List of individual model responses
stage2: List of model rankings
stage3: Final synthesized response
metadata: Optional metadata dict with timing and other info
"""
conversation = get_conversation(conversation_id)
if conversation is None:
raise ValueError(f"Conversation {conversation_id} not found")
message = {
"role": "assistant",
"stage1": stage1,
"stage2": stage2,
"stage3": stage3
}
if metadata:
message["metadata"] = metadata
conversation["messages"].append(message)
save_conversation(conversation)
def update_conversation_title(conversation_id: str, title: str):
"""
Update the title of a conversation.
Args:
conversation_id: Conversation identifier
title: New title for the conversation
"""
conversation = get_conversation(conversation_id)
if conversation is None:
raise ValueError(f"Conversation {conversation_id} not found")
conversation["title"] = title
save_conversation(conversation)
def delete_conversation(conversation_id: str):
"""
Delete a conversation (and its associated documents).
Args:
conversation_id: Conversation identifier
"""
path = get_conversation_path(conversation_id)
if not os.path.exists(path):
raise ValueError(f"Conversation {conversation_id} not found")
# Delete the conversation file
os.remove(path)
# Also delete associated documents directory
from .documents import _conversation_dir
docs_dir = _conversation_dir(conversation_id)
if docs_dir.exists():
import shutil
shutil.rmtree(docs_dir, ignore_errors=True)
def archive_conversation(conversation_id: str, archived: bool = True):
"""
Archive or unarchive a conversation.
Args:
conversation_id: Conversation identifier
archived: True to archive, False to unarchive
"""
conversation = get_conversation(conversation_id)
if conversation is None:
raise ValueError(f"Conversation {conversation_id} not found")
conversation["archived"] = archived
if archived:
conversation["archived_at"] = datetime.utcnow().isoformat()
else:
conversation.pop("archived_at", None)
save_conversation(conversation)

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import importlib
import os
import unittest
class TestConfigEnvOverrides(unittest.TestCase):
def setUp(self):
self._old_env = dict(os.environ)
def tearDown(self):
os.environ.clear()
os.environ.update(self._old_env)
def test_council_models_override_from_env_csv(self):
os.environ["COUNCIL_MODELS"] = "a,b, c"
import backend.config as config
importlib.reload(config)
self.assertEqual(config.COUNCIL_MODELS, ["a", "b", "c"])
def test_chairman_model_override(self):
os.environ["CHAIRMAN_MODEL"] = "chair"
import backend.config as config
importlib.reload(config)
self.assertEqual(config.CHAIRMAN_MODEL, "chair")
def test_max_tokens_override(self):
os.environ["MAX_TOKENS"] = "1234"
import backend.config as config
importlib.reload(config)
self.assertEqual(config.MAX_TOKENS, 1234)

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import importlib
import os
import shutil
import tempfile
import unittest
import httpx
class TestDocPreviewTruncation(unittest.IsolatedAsyncioTestCase):
async def asyncSetUp(self):
self._old_env = dict(os.environ)
self.tmp = tempfile.mkdtemp(prefix="llm-council-docprev-")
os.environ["DOCS_DIR"] = self.tmp
os.environ["MAX_DOC_BYTES"] = "1000000"
os.environ["MAX_DOC_PREVIEW_CHARS"] = "10"
import backend.config as config
import backend.documents as documents
import backend.main as main
importlib.reload(config)
importlib.reload(documents)
self.main = importlib.reload(main)
self.client = httpx.AsyncClient(
transport=httpx.ASGITransport(app=self.main.app),
base_url="http://test",
)
# Create a conversation
resp = await self.client.post("/api/conversations", json={})
resp.raise_for_status()
self.conversation_id = resp.json()["id"]
# Upload a long doc
files = {"file": ("long.md", b"0123456789ABCDEFGHIJ", "text/markdown")}
up = await self.client.post(
f"/api/conversations/{self.conversation_id}/documents",
files=files,
)
up.raise_for_status()
self.doc_id = up.json()["id"]
async def asyncTearDown(self):
await self.client.aclose()
os.environ.clear()
os.environ.update(self._old_env)
shutil.rmtree(self.tmp, ignore_errors=True)
async def test_preview_truncates(self):
resp = await self.client.get(
f"/api/conversations/{self.conversation_id}/documents/{self.doc_id}"
)
self.assertEqual(resp.status_code, 200)
content = resp.json()["content"]
self.assertEqual(len(content), 10)
self.assertTrue(content.endswith("..."))

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import os
import shutil
import tempfile
import unittest
import importlib
import httpx
class TestDocumentsApi(unittest.IsolatedAsyncioTestCase):
async def asyncSetUp(self):
self._old_env = dict(os.environ)
self.tmp = tempfile.mkdtemp(prefix="llm-council-docsapi-")
os.environ["DOCS_DIR"] = self.tmp
os.environ["MAX_DOC_BYTES"] = "1000000"
# Reload config/documents so they see DOCS_DIR override
import backend.config as config
import backend.documents as documents
import backend.main as main
importlib.reload(config)
importlib.reload(documents)
self.main = importlib.reload(main)
self.transport = httpx.ASGITransport(app=self.main.app)
self.client = httpx.AsyncClient(transport=self.transport, base_url="http://test")
# Create a conversation
resp = await self.client.post("/api/conversations", json={})
resp.raise_for_status()
self.conversation_id = resp.json()["id"]
async def asyncTearDown(self):
await self.client.aclose()
os.environ.clear()
os.environ.update(self._old_env)
shutil.rmtree(self.tmp, ignore_errors=True)
async def test_upload_and_list_documents(self):
# Upload
files = {"file": ("notes.md", b"# Hi\n", "text/markdown")}
resp = await self.client.post(
f"/api/conversations/{self.conversation_id}/documents",
files=files,
)
self.assertEqual(resp.status_code, 200, resp.text)
meta = resp.json()
self.assertIn("id", meta)
self.assertEqual(meta["filename"], "notes.md")
# List
resp2 = await self.client.get(
f"/api/conversations/{self.conversation_id}/documents"
)
self.assertEqual(resp2.status_code, 200, resp2.text)
items = resp2.json()
self.assertEqual(len(items), 1)
self.assertEqual(items[0]["id"], meta["id"])
async def test_upload_multiple_documents(self):
files = [
("files", ("a.md", b"one", "text/markdown")),
("files", ("b.md", b"two", "text/markdown")),
]
resp = await self.client.post(
f"/api/conversations/{self.conversation_id}/documents",
files=files,
)
self.assertEqual(resp.status_code, 200, resp.text)
payload = resp.json()
self.assertIn("uploaded", payload)
self.assertEqual(len(payload["uploaded"]), 2)
self.assertEqual({d["filename"] for d in payload["uploaded"]}, {"a.md", "b.md"})
async def test_rejects_non_md(self):
files = {"file": ("notes.txt", b"hello", "text/plain")}
resp = await self.client.post(
f"/api/conversations/{self.conversation_id}/documents",
files=files,
)
self.assertEqual(resp.status_code, 400)
async def test_get_and_delete_document(self):
files = {"file": ("a.md", b"hello", "text/markdown")}
up = await self.client.post(
f"/api/conversations/{self.conversation_id}/documents",
files=files,
)
self.assertEqual(up.status_code, 200)
doc_id = up.json()["id"]
get = await self.client.get(
f"/api/conversations/{self.conversation_id}/documents/{doc_id}"
)
self.assertEqual(get.status_code, 200)
self.assertEqual(get.json()["content"], "hello")
dele = await self.client.delete(
f"/api/conversations/{self.conversation_id}/documents/{doc_id}"
)
self.assertEqual(dele.status_code, 200)
self.assertTrue(dele.json()["ok"])
get2 = await self.client.get(
f"/api/conversations/{self.conversation_id}/documents/{doc_id}"
)
self.assertEqual(get2.status_code, 404)

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import os
import shutil
import tempfile
import unittest
import importlib
class TestDocsContext(unittest.TestCase):
def setUp(self):
self._old_env = dict(os.environ)
self.tmp = tempfile.mkdtemp(prefix="llm-council-docsctx-")
os.environ["DOCS_DIR"] = self.tmp
os.environ["MAX_DOC_BYTES"] = "1000000"
import backend.config as config
import backend.documents as documents
import backend.docs_context as docs_context
self.config = importlib.reload(config)
self.documents = importlib.reload(documents)
self.docs_context = importlib.reload(docs_context)
def tearDown(self):
os.environ.clear()
os.environ.update(self._old_env)
shutil.rmtree(self.tmp, ignore_errors=True)
def test_build_docs_context_truncates(self):
conv = "c1"
self.documents.save_markdown_document(conv, "a.md", b"A" * 50)
self.documents.save_markdown_document(conv, "b.md", b"B" * 50)
ctx = self.docs_context.build_docs_context(conv, max_chars=60, max_docs=5)
self.assertIsNotNone(ctx)
self.assertIn("DOC:", ctx)
self.assertTrue(len(ctx) <= 60)

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import os
import shutil
import tempfile
import unittest
import importlib
class TestDocumentsStorage(unittest.TestCase):
def setUp(self):
self._old_env = dict(os.environ)
self.tmp = tempfile.mkdtemp(prefix="llm-council-docs-")
os.environ["DOCS_DIR"] = self.tmp
os.environ["MAX_DOC_BYTES"] = "100"
import backend.config as config
import backend.documents as documents
self.config = importlib.reload(config)
self.documents = importlib.reload(documents)
def tearDown(self):
os.environ.clear()
os.environ.update(self._old_env)
shutil.rmtree(self.tmp, ignore_errors=True)
def test_save_and_list_document(self):
meta = self.documents.save_markdown_document(
"conv1",
"../weird/name.md",
b"# Hello\n",
)
self.assertTrue(meta.id)
self.assertEqual(meta.filename, "name.md")
self.assertEqual(meta.bytes, 8)
listed = self.documents.list_documents("conv1")
self.assertEqual(len(listed), 1)
self.assertEqual(listed[0].id, meta.id)
self.assertEqual(listed[0].filename, "name.md")
text = self.documents.read_document_text("conv1", meta.id)
self.assertIn("# Hello", text)
def test_rejects_too_large(self):
with self.assertRaises(ValueError):
self.documents.save_markdown_document("conv1", "a.md", b"x" * 101)

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import os
import unittest
class TestProviderSelection(unittest.TestCase):
def setUp(self):
self._old_env = dict(os.environ)
def tearDown(self):
os.environ.clear()
os.environ.update(self._old_env)
def test_always_returns_openai_compat(self):
"""Provider is always 'openai_compat' now (OpenRouter removed)."""
from backend.llm_client import _get_provider_name
# Should always return openai_compat regardless of env vars
self.assertEqual(_get_provider_name(), "openai_compat")
# Test with different env var combinations
os.environ["OPENAI_COMPAT_BASE_URL"] = "http://gpu:8000"
self.assertEqual(_get_provider_name(), "openai_compat")
os.environ.pop("OPENAI_COMPAT_BASE_URL", None)
self.assertEqual(_get_provider_name(), "openai_compat")
class TestParallelConcurrency(unittest.IsolatedAsyncioTestCase):
async def test_query_models_parallel_respects_llm_max_concurrency(self):
import asyncio
import backend.llm_client as lc
old_env = dict(os.environ)
old_query_model = lc.query_model
in_flight = 0
max_in_flight = 0
lock = asyncio.Lock()
async def fake_query_model(model, messages, timeout=120.0, max_tokens_override=None):
nonlocal in_flight, max_in_flight
async with lock:
in_flight += 1
max_in_flight = max(max_in_flight, in_flight)
# ensure overlap is possible without the semaphore
await asyncio.sleep(0.02)
async with lock:
in_flight -= 1
return {"content": model}
try:
os.environ["LLM_MAX_CONCURRENCY"] = "1"
lc.query_model = fake_query_model
models = ["m1", "m2", "m3"]
out = await lc.query_models_parallel(models, [{"role": "user", "content": "hi"}])
self.assertEqual(set(out.keys()), set(models))
self.assertEqual(max_in_flight, 1)
finally:
lc.query_model = old_query_model
os.environ.clear()
os.environ.update(old_env)

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import importlib
import os
import unittest
import httpx
class TestLlmStatusEndpoint(unittest.IsolatedAsyncioTestCase):
async def asyncSetUp(self):
self._old_env = dict(os.environ)
os.environ["OPENAI_COMPAT_BASE_URL"] = "http://localhost:11434"
os.environ.pop("USE_LOCAL_OLLAMA", None) # Clear this so OPENAI_COMPAT_BASE_URL is used
import backend.config as config
import backend.main as main
importlib.reload(config) # Reload config to pick up env changes
self.main = importlib.reload(main)
self.client = httpx.AsyncClient(
transport=httpx.ASGITransport(app=self.main.app),
base_url="http://test",
)
async def asyncTearDown(self):
await self.client.aclose()
os.environ.clear()
os.environ.update(self._old_env)
async def test_status_without_probe(self):
resp = await self.client.get("/api/llm/status")
self.assertEqual(resp.status_code, 200)
data = resp.json()
self.assertEqual(data["provider"], "openai_compat")
self.assertEqual(data["base_url"], "http://localhost:11434")

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import unittest
import httpx
import json
from backend.openai_compat import _resolve_chat_completions_url, _resolve_models_url, query_model, list_models
class TestOpenAICompatUrl(unittest.TestCase):
def test_resolve_url_when_no_v1(self):
self.assertEqual(
_resolve_chat_completions_url("http://gpu:8000"),
"http://gpu:8000/v1/chat/completions",
)
def test_resolve_url_when_v1(self):
self.assertEqual(
_resolve_chat_completions_url("http://gpu:8000/v1"),
"http://gpu:8000/v1/chat/completions",
)
def test_resolve_url_when_v1_with_trailing_slash(self):
self.assertEqual(
_resolve_chat_completions_url("http://gpu:8000/v1/"),
"http://gpu:8000/v1/chat/completions",
)
def test_resolve_models_url(self):
self.assertEqual(
_resolve_models_url("http://gpu:8000"),
"http://gpu:8000/v1/models",
)
class TestOpenAICompatRequest(unittest.IsolatedAsyncioTestCase):
async def test_query_model_builds_payload_and_parses_response(self):
captured = {}
def handler(request: httpx.Request) -> httpx.Response:
captured["url"] = str(request.url)
captured["auth"] = request.headers.get("authorization")
captured["json"] = json.loads(request.content.decode("utf-8"))
return httpx.Response(
200,
json={
"choices": [
{
"message": {"content": "hello", "reasoning_details": None},
}
]
},
)
transport = httpx.MockTransport(handler)
async with httpx.AsyncClient(transport=transport, timeout=10.0) as client:
out = await query_model(
"my-model",
[{"role": "user", "content": "hi"}],
base_url="http://gpu:8000",
api_key="secret",
max_tokens=123,
timeout=10.0,
client=client,
)
self.assertEqual(captured["url"], "http://gpu:8000/v1/chat/completions")
self.assertEqual(captured["auth"], "Bearer secret")
self.assertEqual(captured["json"]["model"], "my-model")
self.assertEqual(captured["json"]["max_tokens"], 123)
self.assertEqual(out["content"], "hello")
async def test_list_models_parses_ids(self):
def handler(request: httpx.Request) -> httpx.Response:
return httpx.Response(
200,
json={"data": [{"id": "a"}, {"id": "b"}, {"nope": "c"}]},
)
transport = httpx.MockTransport(handler)
async with httpx.AsyncClient(transport=transport, timeout=10.0) as client:
ids = await list_models(
base_url="http://gpu:8000",
client=client,
)
self.assertEqual(ids, ["a", "b"])

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# Deployment Guide
## Overview
LLM Council can be deployed in several configurations depending on your needs:
- **Local Development**: Everything runs on your local machine
- **Hybrid**: Frontend/Backend local, LLM server on remote GPU VM
- **Full Remote**: Everything on a server/VM
- **Production**: Professional deployment with proper infrastructure
## Architecture Options
### Option 1: Hybrid (Recommended for Development)
**Setup:**
- Frontend + Backend: Run on your local machine
- LLM Server (Ollama): Run on remote GPU VM
**Pros:**
- Easy development and debugging
- GPU resources available remotely
- No need to deploy frontend/backend code
- Fast iteration
**Cons:**
- Requires network connectivity to GPU VM
- Latency for LLM requests
**Configuration:**
```bash
# .env on local machine
USE_LOCAL_OLLAMA=false
OPENAI_COMPAT_BASE_URL=http://your-gpu-vm-ip:11434
```
### Option 2: Full Remote Deployment
**Setup:**
- Everything runs on the GPU VM or dedicated server
**Pros:**
- Centralized deployment
- Can be accessed from multiple machines
- Better for team use
**Cons:**
- More complex setup
- Requires proper security configuration
- Slower development iteration
### Option 3: Production Deployment (Professional)
**Recommended Stack:**
- **Frontend**: Serve static build via nginx/CDN
- **Backend**: Run via systemd/gunicorn/uvicorn with reverse proxy
- **LLM Server**: Separate service on GPU VM
- **Security**: TLS/HTTPS, authentication, rate limiting
## GPU VM Setup
### Prerequisites
1. GPU VM with:
- NVIDIA GPU with CUDA support
- Sufficient VRAM for your models
- Network access from your local machine
2. Ollama installed on GPU VM:
```bash
curl -fsSL https://ollama.ai/install.sh | sh
```
### Step 1: Configure Ollama to Accept Remote Connections
**On GPU VM:**
```bash
# Option A: Environment variable (temporary)
export OLLAMA_HOST=0.0.0.0:11434
# Option B: Systemd service (persistent - recommended)
sudo systemctl edit ollama
```
Add to the override file:
```ini
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_KEEP_ALIVE=24h"
Environment="OLLAMA_MAX_LOADED_MODELS=3"
```
Then restart:
```bash
sudo systemctl daemon-reload
sudo systemctl restart ollama
```
### Step 2: Configure Firewall
**On GPU VM:**
```bash
# Allow port 11434 from your local network
sudo ufw allow from YOUR_LOCAL_IP to any port 11434
# Or allow from entire subnet (less secure)
sudo ufw allow 11434/tcp
```
### Step 3: Pull Required Models
**On GPU VM:**
```bash
ollama pull qwen2.5:7b
ollama pull llama3.1:8b
ollama pull qwen2.5:14b
ollama pull qwen2:latest
```
### Step 3.5 (GPU VM): Ensure Ollama Uses GPU + Stores Data on /mnt/data
If your VM has a small root disk, keep Ollama's storage and HOME off `/` (common cause of weird failures).
Also note that on this setup, `OLLAMA_LLM_LIBRARY=cuda` caused Ollama to *skip* CUDA libraries; use `cuda_v12`.
**On GPU VM:**
```bash
sudo mkdir -p /etc/systemd/system/ollama.service.d
sudo tee /etc/systemd/system/ollama.service.d/override.conf >/dev/null <<'EOF'
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_KEEP_ALIVE=24h"
Environment="OLLAMA_MODELS=/mnt/data/ollama"
Environment="HOME=/mnt/data/ollama/home"
Environment="OLLAMA_LLM_LIBRARY=cuda_v12"
Environment="LD_LIBRARY_PATH=/usr/local/lib/ollama:/usr/local/lib/ollama/cuda_v12"
EOF
sudo systemctl daemon-reload
sudo systemctl restart ollama
```
**Verify GPU offload (on GPU VM):**
```bash
ollama run qwen2:latest "Write 80 words about GPUs."
ollama ps
```
### Step 4: Verify Remote Access
**From local machine:**
```bash
curl http://YOUR_GPU_VM_IP:11434/api/tags
# Should return list of available models
curl http://YOUR_GPU_VM_IP:11434/v1/models
```
### Step 5: Configure LLM Council
**On local machine `.env`:**
```bash
USE_LOCAL_OLLAMA=false
OPENAI_COMPAT_BASE_URL=http://YOUR_GPU_VM_IP:11434
# Local (small) example:
# COUNCIL_MODELS=llama3.2:1b,qwen2.5:0.5b,gemma2:2b
# CHAIRMAN_MODEL=llama3.2:3b
# GPU (available models):
COUNCIL_MODELS=qwen2.5:7b,llama3.1:8b,qwen2:latest
CHAIRMAN_MODEL=qwen2.5:14b
```
## Security Considerations
### For Development/Internal Use
1. **Network Security:**
- Use VPN or private network
- Restrict firewall to specific IPs
- Consider SSH tunnel for extra security
2. **Ollama Security:**
- Ollama has no built-in authentication
- Only expose on trusted networks
- Consider reverse proxy with auth (nginx + basic auth)
### For Production
1. **Authentication:**
- Add API key authentication to backend
- Use session-based auth for frontend
- Implement rate limiting
2. **Network Security:**
- Use HTTPS/TLS everywhere
- Set up proper firewall rules
- Consider using a reverse proxy (nginx/traefik)
3. **Infrastructure:**
- Use container orchestration (Docker Compose/Kubernetes)
- Set up monitoring and logging
- Implement backup strategy for conversations
## Deployment Scripts
### Quick Start (Local + Remote Ollama)
```bash
# 1. Start Ollama on GPU VM (already running if systemd configured)
# 2. On local machine:
./start.sh
```
### Full Remote Deployment
See `docs/DEPLOYMENT_FULL.md` for complete remote deployment instructions.
## Troubleshooting
### Connection Timeouts
1. Check Ollama is listening on all interfaces:
```bash
# On GPU VM
sudo netstat -tlnp | grep 11434
# Should show 0.0.0.0:11434, not 127.0.0.1:11434
```
2. Check firewall rules:
```bash
# On GPU VM
sudo ufw status
```
3. Test connectivity:
```bash
# From local machine
curl -v http://GPU_VM_IP:11434/api/tags
```
### Model Loading Issues
1. Check available VRAM:
```bash
nvidia-smi
```
2. Adjust `OLLAMA_MAX_LOADED_MODELS` if needed
3. Check model sizes vs available memory
## Performance Tuning
### Ollama Settings
```bash
# On GPU VM, edit systemd override:
Environment="OLLAMA_KEEP_ALIVE=24h" # Keep models loaded
Environment="OLLAMA_MAX_LOADED_MODELS=3" # Max concurrent models
Environment="OLLAMA_NUM_PARALLEL=1" # Parallel requests
```
### LLM Council Timeouts
Adjust in `.env`:
```bash
LLM_TIMEOUT_SECONDS=600.0 # For slow models
CHAIRMAN_TIMEOUT_SECONDS=600.0
OPENAI_COMPAT_TIMEOUT_SECONDS=600.0
OPENAI_COMPAT_CONNECT_TIMEOUT_SECONDS=30.0
OPENAI_COMPAT_WRITE_TIMEOUT_SECONDS=30.0
OPENAI_COMPAT_POOL_TIMEOUT_SECONDS=30.0
```

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# Professional Deployment Recommendations
## Recommended Architecture
### For Development/Personal Use
**Hybrid Approach (Recommended):**
```
┌─────────────────┐ ┌──────────────────┐
│ Local Machine │ │ GPU VM │
│ │ │ │
│ Frontend │ │ Ollama Server │
│ (React/Vite) │ │ (LLM Models) │
│ │◄────────┤ │
│ Backend │ HTTP │ Port 11434 │
│ (FastAPI) │ │ │
└─────────────────┘ └──────────────────┘
```
**Why this is best:**
- ✅ Fast development iteration
- ✅ Easy debugging (logs on local machine)
- ✅ GPU resources available remotely
- ✅ No complex deployment needed
- ✅ Can work offline (if models cached locally)
### For Production/Team Use
**Full Remote Deployment:**
```
┌─────────────────┐
│ Users │
│ (Browsers) │
└────────┬────────┘
│ HTTPS
▼
┌─────────────────┐
│ Reverse Proxy │
│ (nginx/traefik)│
└────────┬────────┘
│
┌────┴────┐
│ │
▼ ▼
┌────────┐ ┌──────────┐
│Frontend│ │ Backend │
│(Static)│ │(FastAPI) │
└────────┘ └────┬─────┘
│ HTTP
▼
┌──────────────┐
│ GPU VM │
│ Ollama │
└──────────────┘
```
## Comparison of Approaches
| Aspect | Hybrid (Local + Remote) | Full Remote | Production |
|--------|------------------------|-------------|------------|
| **Setup Complexity** | Low | Medium | High |
| **Development Speed** | Fast | Medium | Slow |
| **Security** | Medium | Medium | High |
| **Scalability** | Low | Medium | High |
| **Cost** | Low | Medium | High |
| **Best For** | Dev/Personal | Team/Internal | Public/Enterprise |
## Security Best Practices
### Development/Internal Network
1. **Network Isolation:**
- Use private network/VPN
- Restrict firewall to specific IPs
- Consider SSH tunnel for Ollama
2. **Ollama Access:**
```bash
# Only allow from specific IPs
sudo ufw allow from YOUR_IP to any port 11434
```
3. **SSH Tunnel Alternative:**
```bash
# More secure - no direct network exposure
ssh -L 11434:localhost:11434 user@gpu-vm
# Then use localhost:11434 in .env
```
### Production Deployment
1. **Authentication:**
- Add API keys to backend
- Implement user sessions
- Use OAuth2/JWT for API
2. **Network Security:**
- HTTPS/TLS everywhere
- WAF (Web Application Firewall)
- Rate limiting
- DDoS protection
3. **Infrastructure:**
- Container orchestration (Docker/K8s)
- Service mesh for internal communication
- Monitoring and alerting
- Automated backups
## Deployment Checklist
### Hybrid Setup (Recommended for Dev)
- [ ] GPU VM has Ollama installed
- [ ] Ollama configured to listen on 0.0.0.0:11434
- [ ] Firewall allows connections from local machine
- [ ] Models pulled on GPU VM
- [ ] Local `.env` configured with GPU VM IP
- [ ] Test connection: `curl http://GPU_VM_IP:11434/api/tags`
### Full Remote Setup
- [ ] Server/VM provisioned
- [ ] Frontend built and served (nginx/static host)
- [ ] Backend running as service (systemd/supervisor)
- [ ] Reverse proxy configured (nginx/traefik)
- [ ] SSL certificates installed
- [ ] Authentication implemented
- [ ] Monitoring set up
- [ ] Backup strategy in place
### Production Setup
- [ ] All of Full Remote checklist
- [ ] Load balancing configured
- [ ] Database for conversations (optional upgrade)
- [ ] Logging and monitoring (Prometheus/Grafana)
- [ ] CI/CD pipeline
- [ ] Security audit
- [ ] Documentation for ops team
- [ ] Disaster recovery plan
## Cost Considerations
### Hybrid (Local + Remote GPU VM)
- **Cost**: GPU VM only (~$0.50-2/hour depending on GPU)
- **Best for**: Development, personal projects, small teams
### Full Remote
- **Cost**: GPU VM + Application Server (~$1-3/hour)
- **Best for**: Teams, internal tools
### Production
- **Cost**: $100-1000+/month depending on scale
- **Best for**: Public services, enterprise
## Migration Path
1. **Start**: Hybrid (local dev, remote GPU)
2. **Grow**: Full remote (when team needs it)
3. **Scale**: Production (when going public/enterprise)
## Recommendation
**For your use case (development/personal):**
Use the **Hybrid approach**:
- Run frontend + backend locally
- Connect to Ollama on GPU VM
- Use SSH tunnel for extra security if needed
- Simple, fast, cost-effective
This gives you:
- Fast development iteration
- Easy debugging
- GPU resources when needed
- Minimal infrastructure complexity
- Low cost

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# GPU VM Setup - Quick Reference
## Quick Setup Steps
### 1. On GPU VM: Configure Ollama to Accept Remote Connections
```bash
# Create systemd override
sudo mkdir -p /etc/systemd/system/ollama.service.d
sudo tee /etc/systemd/system/ollama.service.d/override.conf > /dev/null <<EOF
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_KEEP_ALIVE=24h"
# Keep Ollama storage off root disk (recommended) and ensure the service user
# has a writable HOME (runners/keys/cache).
Environment="OLLAMA_MODELS=/mnt/data/ollama"
Environment="HOME=/mnt/data/ollama/home"
# IMPORTANT (GPU): on this VM, `OLLAMA_LLM_LIBRARY=cuda` caused Ollama to SKIP CUDA.
# Use the libdir selector instead.
Environment="OLLAMA_LLM_LIBRARY=cuda_v12"
# Ensure the dynamic linker can resolve Ollama's bundled CUDA + ggml libs.
Environment="LD_LIBRARY_PATH=/usr/local/lib/ollama:/usr/local/lib/ollama/cuda_v12"
EOF
# Reload and restart
sudo systemctl daemon-reload
sudo systemctl restart ollama
# Verify
curl http://0.0.0.0:11434/api/tags
```
**Verify GPU is actually used (on GPU VM):**
```bash
ollama run qwen2:latest "Write 80 words about GPUs."
ollama ps
watch -n 0.2 nvidia-smi
```
### 2. On GPU VM: Configure Firewall
```bash
# Allow port 11434 (adjust IP/subnet as needed)
sudo ufw allow from YOUR_LOCAL_IP to any port 11434
# Or allow from entire subnet (less secure)
sudo ufw allow 11434/tcp
```
### 3. On GPU VM: Pull Required Models
```bash
ollama pull qwen2.5:7b
ollama pull llama3.1:8b
ollama pull qwen2.5:14b
ollama pull qwen2:latest
```
### 4. On Local Machine: Configure .env
```bash
USE_LOCAL_OLLAMA=false
OPENAI_COMPAT_BASE_URL=http://YOUR_GPU_VM_IP:11434
# Local (small) example:
# COUNCIL_MODELS=llama3.2:1b,qwen2.5:0.5b,gemma2:2b
# CHAIRMAN_MODEL=llama3.2:3b
# GPU (available models):
COUNCIL_MODELS=qwen2.5:7b,llama3.1:8b,qwen2:latest
CHAIRMAN_MODEL=qwen2.5:14b
```
### 5. Test Connection
```bash
# From local machine
curl http://YOUR_GPU_VM_IP:11434/api/tags
curl http://YOUR_GPU_VM_IP:11434/v1/models
```
## Troubleshooting
- **Connection timeout**: Check Ollama is listening on `0.0.0.0:11434` (not `127.0.0.1`)
- **Firewall blocking**: Check `sudo ufw status` and allow port 11434
- **CPU instead of GPU**: Run `ollama ps` and confirm it doesn't say `100% CPU`. If it does:
- Ensure `OLLAMA_LLM_LIBRARY=cuda_v12` (not `cuda`)
- Ensure `LD_LIBRARY_PATH` includes `/usr/local/lib/ollama` and `/usr/local/lib/ollama/cuda_v12`
- Ensure `OLLAMA_MODELS` and `HOME` are on a disk with free space (root disk full can break runner/cache)
See [DEPLOYMENT.md](DEPLOYMENT.md) for detailed instructions.

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# Documentation
## Getting Started
- **[README](../README.md)** - Main project documentation
- **[Architecture](../ARCHITECTURE.md)** - System architecture overview
## Deployment
- **[Deployment Guide](DEPLOYMENT.md)** - Complete deployment instructions with testing steps
- **[Deployment Recommendations](DEPLOYMENT_RECOMMENDATIONS.md)** - Professional deployment options and best practices
- **[GPU VM Setup](GPU_VM_SETUP.md)** - Quick reference for GPU VM configuration

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# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
lerna-debug.log*
node_modules
dist
dist-ssr
*.local
# Editor directories and files
.vscode/*
!.vscode/extensions.json
.idea
.DS_Store
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?

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# React + Vite
This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
Currently, two official plugins are available:
- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Babel](https://babeljs.io/) (or [oxc](https://oxc.rs) when used in [rolldown-vite](https://vite.dev/guide/rolldown)) for Fast Refresh
- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/) for Fast Refresh
## React Compiler
The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
## Expanding the ESLint configuration
If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project.

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import js from '@eslint/js'
import globals from 'globals'
import reactHooks from 'eslint-plugin-react-hooks'
import reactRefresh from 'eslint-plugin-react-refresh'
import { defineConfig, globalIgnores } from 'eslint/config'
export default defineConfig([
globalIgnores(['dist']),
{
files: ['**/*.{js,jsx}'],
extends: [
js.configs.recommended,
reactHooks.configs.flat.recommended,
reactRefresh.configs.vite,
],
languageOptions: {
ecmaVersion: 2020,
globals: globals.browser,
parserOptions: {
ecmaVersion: 'latest',
ecmaFeatures: { jsx: true },
sourceType: 'module',
},
},
rules: {
'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }],
},
},
])

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<!doctype html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<link rel="icon" type="image/svg+xml" href="/vite.svg" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>frontend</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.jsx"></script>
</body>
</html>

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{
"name": "frontend",
"private": true,
"version": "0.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "vite build",
"lint": "eslint .",
"test": "node --test",
"preview": "vite preview"
},
"dependencies": {
"react": "^19.2.0",
"react-dom": "^19.2.0",
"react-markdown": "^10.1.0"
},
"devDependencies": {
"@eslint/js": "^9.39.1",
"@types/react": "^19.2.5",
"@types/react-dom": "^19.2.3",
"@vitejs/plugin-react": "^4.3.1",
"eslint": "^9.39.1",
"eslint-plugin-react-hooks": "^7.0.1",
"eslint-plugin-react-refresh": "^0.4.24",
"globals": "^16.5.0",
"vite": "^5.4.11"
}
}

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<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="31.88" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 257"><defs><linearGradient id="IconifyId1813088fe1fbc01fb466" x1="-.828%" x2="57.636%" y1="7.652%" y2="78.411%"><stop offset="0%" stop-color="#41D1FF"></stop><stop offset="100%" stop-color="#BD34FE"></stop></linearGradient><linearGradient id="IconifyId1813088fe1fbc01fb467" x1="43.376%" x2="50.316%" y1="2.242%" y2="89.03%"><stop offset="0%" stop-color="#FFEA83"></stop><stop offset="8.333%" stop-color="#FFDD35"></stop><stop offset="100%" stop-color="#FFA800"></stop></linearGradient></defs><path fill="url(#IconifyId1813088fe1fbc01fb466)" d="M255.153 37.938L134.897 252.976c-2.483 4.44-8.862 4.466-11.382.048L.875 37.958c-2.746-4.814 1.371-10.646 6.827-9.67l120.385 21.517a6.537 6.537 0 0 0 2.322-.004l117.867-21.483c5.438-.991 9.574 4.796 6.877 9.62Z"></path><path fill="url(#IconifyId1813088fe1fbc01fb467)" d="M185.432.063L96.44 17.501a3.268 3.268 0 0 0-2.634 3.014l-5.474 92.456a3.268 3.268 0 0 0 3.997 3.378l24.777-5.718c2.318-.535 4.413 1.507 3.936 3.838l-7.361 36.047c-.495 2.426 1.782 4.5 4.151 3.78l15.304-4.649c2.372-.72 4.652 1.36 4.15 3.788l-11.698 56.621c-.732 3.542 3.979 5.473 5.943 2.437l1.313-2.028l72.516-144.72c1.215-2.423-.88-5.186-3.54-4.672l-25.505 4.922c-2.396.462-4.435-1.77-3.759-4.114l16.646-57.705c.677-2.35-1.37-4.583-3.769-4.113Z"></path></svg>
Side by Side

After

Width:  |  Height:  |  Size: 1.5 KiB

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* {
box-sizing: border-box;
}
.app {
display: flex;
height: 100vh;
width: 100vw;
overflow: hidden;
background: var(--bg-primary);
color: var(--text-primary);
transition: background-color 0.2s, color 0.2s;
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen',
'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue',
sans-serif;
}

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import { useState, useEffect } from 'react';
import Sidebar from './components/Sidebar';
import ChatInterface from './components/ChatInterface';
import { api } from './api';
import './App.css';
function App() {
// Theme state - default to light, or load from localStorage
const [theme, setTheme] = useState(() => {
const saved = localStorage.getItem('llm-council-theme');
if (saved) return saved;
const envDefault = (import.meta.env.VITE_DEFAULT_THEME || '').toLowerCase();
if (envDefault === 'dark' || envDefault === 'light') return envDefault;
return 'light';
});
const [conversations, setConversations] = useState([]);
const [currentConversationId, setCurrentConversationId] = useState(null);
const [currentConversation, setCurrentConversation] = useState(null);
const [isLoading, setIsLoading] = useState(false);
const [documents, setDocuments] = useState([]);
const [isUploadingDoc, setIsUploadingDoc] = useState(false);
const [selectedDoc, setSelectedDoc] = useState(null); // {id, filename, bytes}
const [selectedDocContent, setSelectedDocContent] = useState('');
const [isLoadingDoc, setIsLoadingDoc] = useState(false);
const [searchQuery, setSearchQuery] = useState('');
const [filteredConversations, setFilteredConversations] = useState([]);
const [prefillMessage, setPrefillMessage] = useState('');
// Apply theme class to document root
useEffect(() => {
document.documentElement.setAttribute('data-theme', theme);
localStorage.setItem('llm-council-theme', theme);
}, [theme]);
const toggleTheme = () => {
setTheme(prev => prev === 'light' ? 'dark' : 'light');
};
// Load conversations on mount
useEffect(() => {
loadConversations();
}, []);
// Read message from URL once (used to prefill the textarea; not auto-sent)
useEffect(() => {
const urlParams = new URLSearchParams(window.location.search);
const msg = urlParams.get('message');
if (msg) setPrefillMessage(msg);
}, []);
// Check for conversation ID in URL after conversations load
useEffect(() => {
if (conversations.length > 0) {
const urlParams = new URLSearchParams(window.location.search);
const convIdFromUrl = urlParams.get('conversation');
if (convIdFromUrl && !currentConversationId) {
const exists = conversations.some(c => c.id === convIdFromUrl);
if (exists) {
setCurrentConversationId(convIdFromUrl);
} else {
console.warn('Conversation from URL not found:', convIdFromUrl);
alert(`Conversation not found: ${convIdFromUrl}\n\nIt may have been deleted, or you're pointing at a different data directory/server.`);
// Remove the invalid param so we don't keep trying.
urlParams.delete('conversation');
const newQuery = urlParams.toString();
const newUrl = `${window.location.pathname}${newQuery ? `?${newQuery}` : ''}${window.location.hash || ''}`;
window.history.replaceState({}, '', newUrl);
}
}
}
}, [conversations, currentConversationId]);
// Filter conversations based on search
useEffect(() => {
if (!searchQuery.trim()) {
setFilteredConversations(conversations);
return;
}
const search = async () => {
try {
const results = await api.searchConversations(searchQuery);
setFilteredConversations(results);
} catch (error) {
console.error('Search failed:', error);
// Fallback to client-side search
const query = searchQuery.toLowerCase();
const filtered = conversations.filter(conv =>
(conv.title || '').toLowerCase().includes(query)
);
setFilteredConversations(filtered);
}
};
const timeoutId = setTimeout(search, 300); // Debounce
return () => clearTimeout(timeoutId);
}, [searchQuery, conversations]);
// Load conversation details when selected
useEffect(() => {
if (currentConversationId) {
loadConversation(currentConversationId);
loadDocuments(currentConversationId);
}
}, [currentConversationId]);
const loadConversations = async () => {
try {
const convs = await api.listConversations();
setConversations(convs);
} catch (error) {
console.error('Failed to load conversations:', error);
}
};
const loadConversation = async (id) => {
try {
const conv = await api.getConversation(id);
setCurrentConversation(conv);
} catch (error) {
console.error('Failed to load conversation:', error);
}
};
const loadDocuments = async (id) => {
try {
const docs = await api.listDocuments(id);
setDocuments(docs);
// Clear selection if it no longer exists
setSelectedDoc((prev) => {
if (!prev) return prev;
return docs.some((d) => d.id === prev.id) ? prev : null;
});
} catch (error) {
console.error('Failed to load documents:', error);
setDocuments([]);
}
};
const handleNewConversation = async () => {
try {
const newConv = await api.createConversation();
setConversations([
{ id: newConv.id, created_at: newConv.created_at, message_count: 0 },
...conversations,
]);
setCurrentConversationId(newConv.id);
} catch (error) {
console.error('Failed to create conversation:', error);
}
};
const handleSelectConversation = (id) => {
setCurrentConversationId(id);
};
const handleSendMessage = async (content) => {
if (!currentConversationId) return;
setIsLoading(true);
try {
// Optimistically add user message to UI
const userMessage = { role: 'user', content };
setCurrentConversation((prev) => ({
...prev,
messages: [...prev.messages, userMessage],
}));
// Create a partial assistant message that will be updated progressively
const assistantMessage = {
role: 'assistant',
stage1: null,
stage2: null,
stage3: null,
metadata: null,
loading: {
stage1: false,
stage2: false,
stage3: false,
},
};
// Add the partial assistant message
setCurrentConversation((prev) => ({
...prev,
messages: [...prev.messages, assistantMessage],
}));
// Send message with streaming
await api.sendMessageStream(currentConversationId, content, (eventType, event) => {
console.log('[Stream Event]', eventType, event); // Debug logging
switch (eventType) {
case 'stage1_start':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.loading.stage1 = true;
lastMsg.stage1 = []; // Initialize empty array
return { ...prev, messages };
});
break;
case 'stage1_response':
// Individual response completed - add it immediately
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
if (!lastMsg.stage1) lastMsg.stage1 = [];
// Check if this model already exists, if so update it, otherwise add it
const existingIdx = lastMsg.stage1.findIndex(r => r.model === event.model);
if (existingIdx >= 0) {
lastMsg.stage1[existingIdx] = event.response;
} else {
lastMsg.stage1.push(event.response);
}
return { ...prev, messages };
});
break;
case 'stage1_complete':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.stage1 = event.data;
if (!lastMsg.metadata) lastMsg.metadata = {};
lastMsg.metadata.stage1_metadata = event.metadata;
lastMsg.loading.stage1 = false;
return { ...prev, messages };
});
break;
case 'stage2_start':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.loading.stage2 = true;
return { ...prev, messages };
});
break;
case 'stage2_complete':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.stage2 = event.data;
if (!lastMsg.metadata) lastMsg.metadata = {};
lastMsg.metadata = { ...lastMsg.metadata, ...event.metadata };
lastMsg.metadata.stage2_metadata = event.metadata.stage2_metadata;
lastMsg.loading.stage2 = false;
return { ...prev, messages };
});
break;
case 'stage3_start':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.loading.stage3 = true;
return { ...prev, messages };
});
break;
case 'stage3_complete':
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
lastMsg.stage3 = event.data;
if (!lastMsg.metadata) lastMsg.metadata = {};
lastMsg.metadata.stage3_metadata = event.metadata;
if (event.metadata?.duration_seconds && lastMsg.metadata.stage1_metadata && lastMsg.metadata.stage2_metadata) {
const total = (lastMsg.metadata.stage1_metadata.duration_seconds || 0) +
(lastMsg.metadata.stage2_metadata.duration_seconds || 0) +
(event.metadata.duration_seconds || 0);
lastMsg.metadata.total_duration_seconds = Math.round(total * 100) / 100;
}
lastMsg.loading.stage3 = false;
return { ...prev, messages };
});
break;
case 'title_complete':
// Reload conversations to get updated title
loadConversations();
break;
case 'complete':
// Stream complete, reload conversations list and current conversation
loadConversations();
if (currentConversationId) {
// Reload the conversation to get the saved assistant message
setTimeout(() => {
loadConversation(currentConversationId);
}, 100);
}
setIsLoading(false);
break;
case 'error':
console.error('Stream error:', event.message);
alert(`Error: ${event.message}`);
setIsLoading(false);
break;
case 'stage1_response_failed':
// Model failed - show notification
setCurrentConversation((prev) => {
const messages = [...prev.messages];
const lastMsg = messages[messages.length - 1];
if (!lastMsg.failed_models) lastMsg.failed_models = [];
if (!lastMsg.failed_models.includes(event.model)) {
lastMsg.failed_models.push(event.model);
}
return { ...prev, messages };
});
break;
default:
console.log('Unknown event type:', eventType);
}
});
} catch (error) {
console.error('Failed to send message:', error);
// Remove optimistic messages on error
setCurrentConversation((prev) => ({
...prev,
messages: prev.messages.slice(0, -2),
}));
setIsLoading(false);
}
};
const handleUploadDocument = async (files) => {
if (!currentConversationId) return;
setIsUploadingDoc(true);
try {
const list = Array.isArray(files) ? files : [files];
if (list.length <= 1) {
await api.uploadDocument(currentConversationId, list[0]);
} else {
await api.uploadDocuments(currentConversationId, list);
}
await loadDocuments(currentConversationId);
} catch (error) {
console.error('Failed to upload document:', error);
alert(error?.message || 'Failed to upload document');
} finally {
setIsUploadingDoc(false);
}
};
const handleViewDocument = async (doc) => {
if (!currentConversationId) return;
setSelectedDoc(doc);
setIsLoadingDoc(true);
try {
const res = await api.getDocument(currentConversationId, doc.id);
setSelectedDocContent(res.content || '');
} catch (error) {
console.error('Failed to get document:', error);
alert(error?.message || 'Failed to load document');
setSelectedDoc(null);
setSelectedDocContent('');
} finally {
setIsLoadingDoc(false);
}
};
const handleDeleteDocument = async (doc) => {
if (!currentConversationId) return;
const ok = confirm(`Delete ${doc.filename}?`);
if (!ok) return;
try {
await api.deleteDocument(currentConversationId, doc.id);
await loadDocuments(currentConversationId);
if (selectedDoc?.id === doc.id) {
setSelectedDoc(null);
setSelectedDocContent('');
}
} catch (error) {
console.error('Failed to delete document:', error);
alert(error?.message || 'Failed to delete document');
}
};
const handleExportReport = async () => {
if (!currentConversationId) return;
try {
await api.exportConversation(currentConversationId);
} catch (error) {
console.error('Failed to export conversation:', error);
alert(error?.message || 'Failed to export conversation');
}
};
const handleDeleteConversation = async (conversationId, e) => {
e.stopPropagation(); // Prevent selecting the conversation when clicking delete
const ok = confirm('Delete this conversation? This cannot be undone.');
if (!ok) return;
try {
await api.deleteConversation(conversationId);
await loadConversations();
if (currentConversationId === conversationId) {
setCurrentConversationId(null);
setCurrentConversation(null);
}
} catch (error) {
console.error('Failed to delete conversation:', error);
alert(error?.message || 'Failed to delete conversation');
}
};
const handleRenameConversation = async (conversationId, newTitle) => {
try {
await api.updateConversationTitle(conversationId, newTitle);
await loadConversations();
// Update current conversation if it's the one being renamed
if (currentConversationId === conversationId) {
await loadConversation(conversationId);
}
} catch (error) {
console.error('Failed to rename conversation:', error);
alert(error?.message || 'Failed to rename conversation');
}
};
return (
<div className={`app ${theme}`}>
<Sidebar
conversations={searchQuery ? filteredConversations : conversations}
currentConversationId={currentConversationId}
onSelectConversation={handleSelectConversation}
onNewConversation={handleNewConversation}
onDeleteConversation={handleDeleteConversation}
onRenameConversation={handleRenameConversation}
theme={theme}
onToggleTheme={toggleTheme}
searchQuery={searchQuery}
onSearchChange={setSearchQuery}
/>
<ChatInterface
conversation={currentConversation}
onSendMessage={handleSendMessage}
isLoading={isLoading}
prefillMessage={prefillMessage}
onPrefillConsumed={() => setPrefillMessage('')}
documents={documents}
isUploadingDoc={isUploadingDoc}
onUploadDocument={handleUploadDocument}
selectedDoc={selectedDoc}
selectedDocContent={selectedDocContent}
isLoadingDoc={isLoadingDoc}
onViewDocument={handleViewDocument}
onDeleteDocument={handleDeleteDocument}
onExportReport={handleExportReport}
/>
</div>
);
}
export default App;

286
frontend/src/api.js Normal file
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/**
* API client for the LLM Council backend.
*/
const API_BASE = 'http://localhost:8001';
export const api = {
/**
* List all conversations.
*/
async listConversations() {
const response = await fetch(`${API_BASE}/api/conversations`);
if (!response.ok) {
throw new Error('Failed to list conversations');
}
return response.json();
},
/**
* Create a new conversation.
*/
async createConversation() {
const response = await fetch(`${API_BASE}/api/conversations`, {
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({}),
});
if (!response.ok) {
throw new Error('Failed to create conversation');
}
return response.json();
},
/**
* Get a specific conversation.
*/
async getConversation(conversationId) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}`
);
if (!response.ok) {
throw new Error('Failed to get conversation');
}
return response.json();
},
/**
* Delete a conversation.
*/
async deleteConversation(conversationId) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}`,
{ method: 'DELETE' }
);
if (!response.ok) {
throw new Error('Failed to delete conversation');
}
return response.json();
},
/**
* Update conversation title.
*/
async updateConversationTitle(conversationId, title) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/title`,
{
method: 'PATCH',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ title }),
}
);
if (!response.ok) {
throw new Error('Failed to update conversation title');
}
return response.json();
},
/**
* Search conversations by title and content.
*/
async searchConversations(query) {
const response = await fetch(
`${API_BASE}/api/conversations/search?q=${encodeURIComponent(query)}`
);
if (!response.ok) {
throw new Error('Failed to search conversations');
}
return response.json();
},
/**
* List uploaded markdown documents for a conversation.
*/
async listDocuments(conversationId) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/documents`
);
if (!response.ok) {
throw new Error('Failed to list documents');
}
return response.json();
},
/**
* Upload a markdown (.md) document for a conversation.
*/
async uploadDocument(conversationId, file) {
// Keep single-file backward compatibility (server accepts "file")
const form = new FormData();
// Support both File and Blob (handy for tests and some environments)
form.append('file', file, file?.name || 'document.md');
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/documents`,
{
method: 'POST',
body: form,
}
);
if (!response.ok) {
const text = await response.text().catch(() => '');
throw new Error(text || 'Failed to upload document');
}
return response.json();
},
/**
* Upload multiple markdown (.md) documents for a conversation in one request.
* Returns either { uploaded: [...] } or a single {id, filename, bytes} if only one file was uploaded.
*/
async uploadDocuments(conversationId, files) {
const list = Array.isArray(files) ? files : [files];
const form = new FormData();
for (const f of list) {
form.append('files', f, f?.name || 'document.md');
}
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/documents`,
{
method: 'POST',
body: form,
}
);
if (!response.ok) {
const text = await response.text().catch(() => '');
throw new Error(text || 'Failed to upload documents');
}
return response.json();
},
/**
* Fetch a document's markdown content.
*/
async getDocument(conversationId, docId) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/documents/${docId}`
);
if (!response.ok) {
throw new Error('Failed to get document');
}
return response.json();
},
/**
* Delete a document.
*/
async deleteDocument(conversationId, docId) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/documents/${docId}`,
{ method: 'DELETE' }
);
if (!response.ok) {
throw new Error('Failed to delete document');
}
return response.json();
},
/**
* Export conversation as markdown report.
*/
async exportConversation(conversationId) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/export`
);
if (!response.ok) {
throw new Error('Failed to export conversation');
}
const blob = await response.blob();
const url = window.URL.createObjectURL(blob);
const a = document.createElement('a');
a.href = url;
const contentDisposition = response.headers.get('Content-Disposition');
if (contentDisposition) {
const filenameMatch = contentDisposition.match(/filename="(.+)"/);
if (filenameMatch) {
a.download = filenameMatch[1];
}
} else {
a.download = `conversation_${conversationId}.md`;
}
document.body.appendChild(a);
a.click();
window.URL.revokeObjectURL(url);
document.body.removeChild(a);
},
/**
* Send a message in a conversation.
*/
async sendMessage(conversationId, content) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/message`,
{
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ content }),
}
);
if (!response.ok) {
throw new Error('Failed to send message');
}
return response.json();
},
/**
* Send a message and receive streaming updates.
* @param {string} conversationId - The conversation ID
* @param {string} content - The message content
* @param {function} onEvent - Callback function for each event: (eventType, data) => void
* @returns {Promise<void>}
*/
async sendMessageStream(conversationId, content, onEvent) {
const response = await fetch(
`${API_BASE}/api/conversations/${conversationId}/message/stream`,
{
method: 'POST',
headers: {
'Content-Type': 'application/json',
},
body: JSON.stringify({ content }),
}
);
if (!response.ok) {
throw new Error('Failed to send message');
}
const reader = response.body.getReader();
const decoder = new TextDecoder();
while (true) {
const { done, value } = await reader.read();
if (done) {
console.log('[Stream] Done reading');
break;
}
const chunk = decoder.decode(value);
const lines = chunk.split('\n');
for (const line of lines) {
if (line.startsWith('data: ')) {
const data = line.slice(6);
try {
const event = JSON.parse(data);
console.log('[Stream] Received event:', event.type);
onEvent(event.type, event);
} catch (e) {
console.error('Failed to parse SSE event:', e, 'Raw data:', data);
}
} else if (line.trim()) {
console.log('[Stream] Non-data line:', line);
}
}
}
},
};

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frontend/src/assets/react.svg Normal file
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Side by Side

After

Width:  |  Height:  |  Size: 4.0 KiB

460
frontend/src/components/ChatInterface.css Normal file
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.chat-interface {
flex: 1;
display: flex;
flex-direction: column;
height: 100vh;
background: var(--bg-primary);
transition: background-color 0.2s;
}
.docs-bar {
display: flex;
align-items: center;
justify-content: space-between;
gap: 12px;
padding: 14px 16px;
border-bottom: 1px solid var(--border-light);
background: var(--bg-tertiary);
transition: background-color 0.2s, border-color 0.2s;
}
.docs-left {
display: flex;
flex-direction: column;
gap: 2px;
}
.docs-title {
font-size: 12px;
font-weight: 700;
letter-spacing: 0.4px;
text-transform: uppercase;
color: var(--text-primary);
}
.docs-subtitle {
font-size: 12px;
color: var(--text-secondary);
}
.docs-right {
display: flex;
gap: 8px;
align-items: center;
}
.docs-export-btn,
.docs-upload-btn {
padding: 10px 14px;
border-radius: 8px;
border: 1px solid var(--border-color);
background: var(--bg-primary);
color: var(--text-primary);
font-weight: 600;
cursor: pointer;
font-size: 13px;
transition: background-color 0.2s, border-color 0.2s;
}
.docs-export-btn:hover,
.docs-upload-btn:hover {
background: var(--hover-bg);
}
.docs-upload-btn:disabled,
.docs-export-btn:disabled {
opacity: 0.6;
cursor: not-allowed;
}
.docs-list {
padding: 10px 16px;
border-bottom: 1px solid var(--border-light);
background: var(--bg-primary);
display: flex;
flex-direction: column;
gap: 6px;
transition: background-color 0.2s, border-color 0.2s;
}
.doc-item {
display: flex;
justify-content: space-between;
gap: 12px;
font-size: 13px;
color: var(--text-primary);
}
.doc-name-btn {
font-weight: 700;
border: 1px solid transparent;
background: transparent;
padding: 4px 8px;
border-radius: 8px;
text-align: left;
cursor: pointer;
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
display: flex;
align-items: center;
gap: 6px;
color: var(--text-primary);
}
.doc-number {
font-weight: 600;
color: #4a90e2;
font-size: 12px;
background: rgba(74, 144, 226, 0.1);
padding: 2px 6px;
border-radius: 4px;
flex-shrink: 0;
}
.doc-name-btn:hover {
background: var(--hover-bg);
}
.doc-name-btn.active {
border-color: var(--border-hover);
background: var(--hover-bg-light);
}
.doc-actions {
display: flex;
align-items: center;
gap: 10px;
flex-shrink: 0;
}
.doc-bytes {
color: var(--text-secondary);
font-variant-numeric: tabular-nums;
flex-shrink: 0;
}
.doc-delete-btn {
padding: 6px 10px;
border-radius: 8px;
border: 1px solid var(--border-color);
background: var(--bg-primary);
color: var(--text-primary);
cursor: pointer;
font-weight: 600;
transition: background-color 0.2s, border-color 0.2s;
}
.doc-delete-btn:hover {
border-color: #ffb4b4;
background: #fff5f5;
}
.doc-preview {
border-bottom: 1px solid var(--border-light);
background: var(--bg-primary);
transition: background-color 0.2s, border-color 0.2s;
}
.doc-preview-header {
padding: 10px 16px;
display: flex;
justify-content: space-between;
gap: 12px;
border-bottom: 1px solid var(--border-light);
}
.doc-preview-title {
font-weight: 800;
font-size: 13px;
color: var(--text-primary);
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
}
.doc-preview-meta {
font-size: 12px;
color: var(--text-secondary);
flex-shrink: 0;
}
.doc-preview-body {
padding: 12px 16px;
max-height: 240px;
overflow: auto;
}
.doc-preview-loading {
color: var(--text-secondary);
font-style: italic;
}
.messages-container {
flex: 1;
overflow-y: auto;
padding: 24px;
}
.empty-state {
display: flex;
flex-direction: column;
align-items: center;
justify-content: center;
height: 100%;
color: var(--text-secondary);
text-align: center;
}
.empty-state h2 {
margin: 0 0 8px 0;
font-size: 24px;
color: var(--text-primary);
}
.empty-state p {
margin: 0;
font-size: 16px;
}
.message-group {
margin-bottom: 32px;
}
.user-message,
.assistant-message {
margin-bottom: 16px;
}
.message-label {
font-size: 12px;
font-weight: 600;
color: var(--text-secondary);
margin-bottom: 8px;
text-transform: uppercase;
letter-spacing: 0.5px;
}
.user-message .message-content {
background: var(--hover-bg-light);
padding: 16px;
border-radius: 8px;
border: 1px solid var(--border-hover);
color: var(--text-primary);
transition: background-color 0.2s, border-color 0.2s;
line-height: 1.6;
max-width: 80%;
white-space: pre-wrap;
}
.loading-indicator {
display: flex;
align-items: center;
gap: 12px;
padding: 16px;
color: var(--text-secondary);
font-size: 14px;
}
.stage-loading {
display: flex;
align-items: center;
gap: 12px;
padding: 16px;
margin: 12px 0;
background: var(--bg-secondary);
border-radius: 8px;
border: 1px solid var(--border-color);
color: var(--text-secondary);
font-size: 14px;
font-style: italic;
transition: background-color 0.2s, border-color 0.2s;
}
.spinner {
width: 20px;
height: 20px;
border: 2px solid #e0e0e0;
border-top-color: #4a90e2;
border-radius: 50%;
animation: spin 0.8s linear infinite;
}
@keyframes spin {
to {
transform: rotate(360deg);
}
}
.input-form {
display: flex;
align-items: flex-end;
gap: 12px;
padding: 24px;
border-top: 1px solid var(--border-color);
background: var(--bg-secondary);
transition: background-color 0.2s, border-color 0.2s;
}
.input-wrapper {
flex: 1;
position: relative;
}
.message-input {
width: 100%;
padding: 16px;
background: var(--bg-primary);
color: var(--text-primary);
border: 1px solid var(--border-color);
border-radius: 8px;
transition: background-color 0.2s, border-color 0.2s, color 0.2s;
font-size: 16px;
font-family: inherit;
line-height: 1.6;
outline: none;
resize: vertical;
min-height: 120px;
max-height: 400px;
box-sizing: border-box;
}
.message-input:focus {
border-color: #4a90e2;
box-shadow: 0 0 0 3px rgba(74, 144, 226, 0.1);
}
.message-input:disabled {
opacity: 0.5;
cursor: not-allowed;
background: var(--bg-secondary);
}
.send-button {
padding: 14px 28px;
background: #4a90e2;
border: 1px solid #4a90e2;
border-radius: 8px;
color: #fff;
font-size: 15px;
font-weight: 600;
cursor: pointer;
transition: background 0.2s;
white-space: nowrap;
align-self: flex-end;
}
.send-button:hover:not(:disabled) {
background: #357abd;
border-color: #357abd;
}
.send-button:disabled {
opacity: 0.5;
cursor: not-allowed;
background: #ccc;
border-color: #ccc;
}
.debug-console {
border-top: 2px solid var(--border-color);
background: var(--bg-secondary);
max-height: 300px;
display: flex;
flex-direction: column;
transition: background-color 0.2s, border-color 0.2s;
}
.debug-console-header {
padding: 8px 12px;
border-bottom: 1px solid var(--border-color);
display: flex;
justify-content: space-between;
align-items: center;
font-size: 12px;
color: var(--text-secondary);
}
.debug-console-header button {
padding: 4px 8px;
font-size: 11px;
background: var(--bg-primary);
border: 1px solid var(--border-color);
border-radius: 4px;
color: var(--text-primary);
cursor: pointer;
}
.debug-console-content {
flex: 1;
overflow: auto;
padding: 12px;
font-size: 11px;
font-family: monospace;
color: var(--text-primary);
}
.debug-console-content pre {
margin: 0;
white-space: pre-wrap;
word-break: break-all;
}
.input-wrapper {
flex: 1;
position: relative;
}
.autocomplete-dropdown {
position: absolute;
background: var(--bg-primary);
border: 1px solid var(--border-color);
border-radius: 8px;
box-shadow: 0 4px 12px rgba(0, 0, 0, 0.15);
z-index: 1000;
max-height: 200px;
overflow-y: auto;
min-width: 300px;
bottom: calc(100% + 8px);
left: 0;
}
.autocomplete-item {
display: flex;
align-items: center;
gap: 8px;
padding: 10px 12px;
cursor: pointer;
border-bottom: 1px solid var(--border-light);
transition: background-color 0.15s;
}
.autocomplete-item:last-child {
border-bottom: none;
}
.autocomplete-item:hover,
.autocomplete-item.selected {
background: var(--hover-bg);
}
.autocomplete-number {
font-weight: 600;
color: #4a90e2;
font-size: 13px;
background: rgba(74, 144, 226, 0.1);
padding: 3px 8px;
border-radius: 4px;
flex-shrink: 0;
min-width: 32px;
text-align: center;
}
.autocomplete-filename {
flex: 1;
overflow: hidden;
text-overflow: ellipsis;
white-space: nowrap;
font-size: 14px;
color: var(--text-primary);
}

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import { useState, useEffect, useRef } from 'react';
import ReactMarkdown from 'react-markdown';
import Stage1 from './Stage1';
import Stage2 from './Stage2';
import Stage3 from './Stage3';
import './ChatInterface.css';
export default function ChatInterface({
conversation,
onSendMessage,
isLoading,
prefillMessage = '',
onPrefillConsumed,
documents = [],
isUploadingDoc = false,
onUploadDocument,
selectedDoc = null,
selectedDocContent = '',
isLoadingDoc = false,
onViewDocument,
onDeleteDocument,
onExportReport,
}) {
const [input, setInput] = useState('');
const [showAutocomplete, setShowAutocomplete] = useState(false);
const [selectedAutocompleteIndex, setSelectedAutocompleteIndex] = useState(0);
const messagesEndRef = useRef(null);
const fileInputRef = useRef(null);
const messageInputRef = useRef(null);
const autocompleteRef = useRef(null);
const scrollToBottom = () => {
messagesEndRef.current?.scrollIntoView({ behavior: 'smooth' });
};
useEffect(() => {
scrollToBottom();
}, [conversation]);
// Auto-focus input when a new conversation is created or selected
useEffect(() => {
if (conversation && !isLoading) {
// Small delay to ensure the textarea is rendered
const timer = setTimeout(() => {
messageInputRef.current?.focus();
}, 100);
return () => clearTimeout(timer);
}
}, [conversation?.id, isLoading]);
// Prefill input (e.g. from make test-setup URL param). Never overrides non-empty input.
useEffect(() => {
if (!conversation?.id) return;
if (!prefillMessage) return;
if (input.trim() !== '') return;
setInput(prefillMessage);
onPrefillConsumed?.();
// Ensure caret at end
setTimeout(() => {
const el = messageInputRef.current;
if (!el) return;
el.focus();
const end = el.value.length;
el.setSelectionRange(end, end);
}, 0);
}, [conversation?.id, prefillMessage, input, onPrefillConsumed]);
// Close autocomplete when clicking outside
useEffect(() => {
const handleClickOutside = (event) => {
if (
autocompleteRef.current &&
!autocompleteRef.current.contains(event.target) &&
messageInputRef.current &&
!messageInputRef.current.contains(event.target)
) {
setShowAutocomplete(false);
}
};
if (showAutocomplete) {
document.addEventListener('mousedown', handleClickOutside);
return () => {
document.removeEventListener('mousedown', handleClickOutside);
};
}
}, [showAutocomplete]);
const handleSubmit = (e) => {
e.preventDefault();
if (input.trim() && !isLoading) {
onSendMessage(input);
setInput('');
}
};
const handleKeyDown = (e) => {
// Handle autocomplete navigation
if (showAutocomplete) {
if (e.key === 'ArrowDown') {
e.preventDefault();
setSelectedAutocompleteIndex((prev) =>
Math.min(prev + 1, documents.length - 1)
);
return;
}
if (e.key === 'ArrowUp') {
e.preventDefault();
setSelectedAutocompleteIndex((prev) => Math.max(prev - 1, 0));
return;
}
if (e.key === 'Enter' || e.key === 'Tab') {
e.preventDefault();
insertDocumentReference(selectedAutocompleteIndex);
return;
}
if (e.key === 'Escape') {
e.preventDefault();
setShowAutocomplete(false);
return;
}
}
// Submit on Enter (without Shift)
if (e.key === 'Enter' && !e.shiftKey && !showAutocomplete) {
e.preventDefault();
handleSubmit(e);
}
};
const insertDocumentReference = (index) => {
const ref = `@${index + 1}`;
const textarea = messageInputRef.current;
if (!textarea) return;
const cursorPos = textarea.selectionStart;
const textBefore = input.substring(0, cursorPos);
const textAfter = input.substring(cursorPos);
// Find the @ symbol position
const atPos = textBefore.lastIndexOf('@');
if (atPos === -1) return;
// Replace from @ to cursor with the reference
const newText = input.substring(0, atPos) + ref + ' ' + textAfter;
setInput(newText);
setShowAutocomplete(false);
// Set cursor position after the inserted reference
setTimeout(() => {
const newCursorPos = atPos + ref.length + 1;
textarea.setSelectionRange(newCursorPos, newCursorPos);
textarea.focus();
}, 0);
};
const handleInputChange = (e) => {
const value = e.target.value;
setInput(value);
const cursorPos = e.target.selectionStart;
const textBefore = value.substring(0, cursorPos);
const lastAt = textBefore.lastIndexOf('@');
// Check if we're typing after an @ symbol
if (lastAt !== -1) {
const textAfterAt = textBefore.substring(lastAt + 1);
// Only show autocomplete if there's no space or newline after @
if (!textAfterAt.match(/[\s\n]/) && documents.length > 0) {
setShowAutocomplete(true);
setSelectedAutocompleteIndex(0);
} else {
setShowAutocomplete(false);
}
} else {
setShowAutocomplete(false);
}
};
if (!conversation) {
return (
<div className="chat-interface">
<div className="empty-state">
<h2>Welcome to LLM Council</h2>
<p>Create a new conversation to get started</p>
</div>
</div>
);
}
const handlePickFile = () => {
if (!fileInputRef.current) return;
fileInputRef.current.value = '';
fileInputRef.current.click();
};
const handleFileChange = async (e) => {
const selected = Array.from(e.target.files || []);
if (selected.length === 0) return;
const nonMd = selected.find((f) => !f.name.toLowerCase().endsWith('.md'));
if (nonMd) {
alert('Only .md files are supported');
return;
}
if (onUploadDocument) {
await onUploadDocument(selected);
}
};
return (
<div className="chat-interface">
<div className="docs-bar">
<div className="docs-left">
<div className="docs-title">Documents</div>
<div className="docs-subtitle">
{documents.length === 0
? 'No .md files attached'
: `${documents.length} attached`}
</div>
</div>
<div className="docs-right">
<input
ref={fileInputRef}
type="file"
multiple
accept=".md,text/markdown"
style={{ display: 'none' }}
onChange={handleFileChange}
/>
{onExportReport && conversation?.messages?.length > 0 && (
<button
type="button"
className="docs-export-btn"
onClick={() => onExportReport()}
disabled={isLoading}
title="Export conversation as markdown report"
>
📄 Export Report
</button>
)}
<button
type="button"
className="docs-upload-btn"
onClick={handlePickFile}
disabled={isUploadingDoc || isLoading}
>
{isUploadingDoc ? 'Uploading…' : 'Upload .md'}
</button>
</div>
</div>
{documents.length > 0 && (
<div className="docs-list">
{documents.map((d, index) => (
<div key={d.id} className="doc-item">
<button
type="button"
className={`doc-name-btn ${selectedDoc?.id === d.id ? 'active' : ''}`}
onClick={() => onViewDocument && onViewDocument(d)}
title="View document"
>
<span className="doc-number">@{index + 1}</span> {d.filename}
</button>
<div className="doc-actions">
<span className="doc-bytes">{d.bytes} bytes</span>
<button
type="button"
className="doc-delete-btn"
onClick={() => onDeleteDocument && onDeleteDocument(d)}
title="Remove document"
>
Remove
</button>
</div>
</div>
))}
</div>
)}
{selectedDoc && (
<div className="doc-preview">
<div className="doc-preview-header">
<div className="doc-preview-title">{selectedDoc.filename}</div>
<div className="doc-preview-meta">{selectedDoc.bytes} bytes</div>
</div>
<div className="doc-preview-body">
{isLoadingDoc ? (
<div className="doc-preview-loading">Loading…</div>
) : (
<div className="markdown-content">
<ReactMarkdown>{selectedDocContent}</ReactMarkdown>
</div>
)}
</div>
</div>
)}
<div className="messages-container">
{conversation.messages.length === 0 ? (
<div className="empty-state">
<h2>Start a conversation</h2>
<p>Ask a question to consult the LLM Council</p>
</div>
) : (
conversation.messages.map((msg, index) => (
<div key={index} className="message-group">
{msg.role === 'user' ? (
<div className="user-message">
<div className="message-label">You</div>
<div className="message-content">
<div className="markdown-content">
<ReactMarkdown>{msg.content}</ReactMarkdown>
</div>
</div>
</div>
) : (
<div className="assistant-message">
<div className="message-label">LLM Council</div>
{/* Stage 1 */}
{msg.loading?.stage1 && (
<div className="stage-loading">
<div className="spinner"></div>
<span>Running Stage 1: Collecting individual responses...</span>
</div>
)}
{msg.stage1 && (
<Stage1
responses={msg.stage1}
metadata={msg.metadata?.stage1_metadata}
/>
)}
{/* Stage 2 */}
{msg.loading?.stage2 && (
<div className="stage-loading">
<div className="spinner"></div>
<span>Running Stage 2: Peer rankings...</span>
</div>
)}
{msg.stage2 && (
<Stage2
rankings={msg.stage2}
labelToModel={msg.metadata?.label_to_model}
aggregateRankings={msg.metadata?.aggregate_rankings}
metadata={msg.metadata?.stage2_metadata}
/>
)}
{/* Stage 3 */}
{msg.loading?.stage3 && (
<div className="stage-loading">
<div className="spinner"></div>
<span>Running Stage 3: Final synthesis...</span>
</div>
)}
{msg.stage3 && (
<Stage3
finalResponse={msg.stage3}
metadata={msg.metadata?.stage3_metadata}
totalDuration={msg.metadata?.total_duration_seconds}
/>
)}
</div>
)}
</div>
))
)}
{isLoading && (
<div className="loading-indicator">
<div className="spinner"></div>
<span>Consulting the council...</span>
</div>
)}
<div ref={messagesEndRef} />
</div>
<form className="input-form" onSubmit={handleSubmit}>
<div className="input-wrapper">
<textarea
ref={messageInputRef}
className="message-input"
placeholder="Ask your question... (Type @ to reference documents, Shift+Enter for new line, Enter to send)"
value={input}
onChange={handleInputChange}
onKeyDown={handleKeyDown}
disabled={isLoading}
/>
{showAutocomplete && documents.length > 0 && (
<div
ref={autocompleteRef}
className="autocomplete-dropdown"
>
{documents.map((doc, index) => (
<div
key={doc.id}
className={`autocomplete-item ${
selectedAutocompleteIndex === index ? 'selected' : ''
}`}
onClick={() => insertDocumentReference(index)}
onMouseEnter={() => setSelectedAutocompleteIndex(index)}
>
<span className="autocomplete-number">@{index + 1}</span>
<span className="autocomplete-filename">{doc.filename}</span>
</div>
))}
</div>
)}
</div>
<button
type="submit"
className="send-button"
disabled={!input.trim() || isLoading}
>
Send
</button>
</form>
</div>
);
}

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.sidebar {
width: 260px;
background: var(--bg-secondary);
border-right: 1px solid var(--border-color);
display: flex;
flex-direction: column;
height: 100vh;
transition: background-color 0.2s, border-color 0.2s;
}
.sidebar-header {
padding: 16px;
border-bottom: 1px solid var(--border-color);
}
.sidebar-header-top {
display: flex;
justify-content: space-between;
align-items: center;
width: 100%;
margin-bottom: 12px;
}
.sidebar-header-actions {
display: flex;
gap: 6px;
align-items: center;
}
.sidebar-header h1 {
font-size: 18px;
margin: 0;
color: var(--text-primary);
}
.theme-toggle-btn,
.debug-toggle-btn {
background: var(--bg-primary);
border: 1px solid var(--border-color);
border-radius: 6px;
padding: 6px 10px;
font-size: 16px;
cursor: pointer;
transition: background 0.2s, border-color 0.2s;
line-height: 1;
color: var(--text-primary);
min-width: 36px;
display: flex;
align-items: center;
justify-content: center;
}
.theme-toggle-btn:hover,
.debug-toggle-btn:hover {
background: var(--hover-bg);
border-color: var(--active-border);
}
.debug-toggle-btn {
font-size: 14px;
}
.new-conversation-btn {
width: 100%;
padding: 10px;
background: #4a90e2;
border: 1px solid #4a90e2;
border-radius: 6px;
color: #fff;
cursor: pointer;
font-size: 14px;
transition: background 0.2s;
font-weight: 500;
}
.new-conversation-btn:hover {
background: #357abd;
border-color: #357abd;
}
.search-box {
padding: 12px 16px;
border-bottom: 1px solid var(--border-color);
}
.search-input {
width: 100%;
padding: 8px 12px;
background: var(--bg-primary);
border: 1px solid var(--border-color);
border-radius: 6px;
color: var(--text-primary);
font-size: 14px;
outline: none;
transition: border-color 0.2s;
}
.search-input:focus {
border-color: #4a90e2;
box-shadow: 0 0 0 2px rgba(74, 144, 226, 0.1);
}
.search-input::placeholder {
color: var(--text-tertiary);
}
.conversation-list {
flex: 1;
overflow-y: auto;
padding: 8px;
}
.no-conversations {
padding: 16px;
text-align: center;
color: var(--text-tertiary);
font-size: 14px;
}
.conversation-item {
padding: 12px;
margin-bottom: 4px;
border-radius: 6px;
cursor: pointer;
transition: background 0.2s;
display: flex;
justify-content: space-between;
align-items: center;
}
.conversation-item:hover {
background: var(--hover-bg);
}
.conversation-item.active {
background: var(--hover-bg-light);
border: 1px solid #4a90e2;
}
.conversation-content {
flex: 1;
min-width: 0;
}
.conversation-title {
color: var(--text-primary);
font-size: 14px;
margin-bottom: 4px;
word-wrap: break-word;
}
.conversation-meta {
color: var(--text-tertiary);
font-size: 12px;
}
.conversation-actions {
display: flex;
gap: 4px;
margin-left: 8px;
opacity: 0;
transition: opacity 0.2s;
}
.conversation-item:hover .conversation-actions {
opacity: 1;
}
.conversation-delete-btn {
background: transparent;
border: none;
cursor: pointer;
padding: 4px 8px;
border-radius: 4px;
font-size: 16px;
color: var(--text-secondary);
transition: background 0.2s, color 0.2s;
line-height: 1;
}
.conversation-delete-btn:hover {
background: #fee;
color: #c33;
}
.conversation-rename-btn {
background: transparent;
border: none;
cursor: pointer;
padding: 4px 8px;
border-radius: 4px;
font-size: 14px;
color: var(--text-secondary);
transition: background 0.2s, color 0.2s;
line-height: 1;
}
.conversation-rename-btn:hover {
background: var(--hover-bg);
color: var(--text-primary);
}
.conversation-title-edit {
width: 100%;
padding: 4px 8px;
background: var(--bg-primary);
border: 1px solid #4a90e2;
border-radius: 4px;
color: var(--text-primary);
font-size: 14px;
font-family: inherit;
outline: none;
}

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import { useState, useEffect } from 'react';
import './Sidebar.css';
export default function Sidebar({
conversations,
currentConversationId,
onSelectConversation,
onNewConversation,
onDeleteConversation,
onRenameConversation,
theme = 'light',
onToggleTheme,
searchQuery = '',
onSearchChange,
}) {
const [editingId, setEditingId] = useState(null);
const [editValue, setEditValue] = useState('');
const handleStartEdit = (conv, e) => {
e.stopPropagation();
setEditingId(conv.id);
setEditValue(conv.title || 'New Conversation');
};
const handleSaveEdit = async (convId, e) => {
e.stopPropagation();
if (onRenameConversation && editValue.trim()) {
await onRenameConversation(convId, editValue.trim());
}
setEditingId(null);
setEditValue('');
};
const handleCancelEdit = (e) => {
e.stopPropagation();
setEditingId(null);
setEditValue('');
};
const handleKeyDown = (e, convId) => {
if (e.key === 'Enter') {
handleSaveEdit(convId, e);
} else if (e.key === 'Escape') {
handleCancelEdit(e);
}
};
return (
<div className="sidebar">
<div className="sidebar-header">
<div className="sidebar-header-top">
<h1>LLM Council</h1>
<div className="sidebar-header-actions">
{onToggleTheme && (
<button
className="theme-toggle-btn"
onClick={onToggleTheme}
title={theme === 'light' ? 'Switch to dark mode' : 'Switch to light mode'}
>
{theme === 'light' ? '🌙' : '☀️'}
</button>
)}
</div>
</div>
<button className="new-conversation-btn" onClick={onNewConversation}>
+ New Conversation
</button>
</div>
<div className="search-box">
<input
type="text"
className="search-input"
placeholder="Search conversations..."
value={searchQuery}
onChange={(e) => onSearchChange && onSearchChange(e.target.value)}
/>
</div>
<div className="conversation-list">
{conversations.length === 0 ? (
<div className="no-conversations">No conversations yet</div>
) : (
conversations.map((conv) => (
<div
key={conv.id}
className={`conversation-item ${
conv.id === currentConversationId ? 'active' : ''
}`}
onClick={() => onSelectConversation(conv.id)}
>
<div className="conversation-content">
{editingId === conv.id ? (
<input
type="text"
className="conversation-title-edit"
value={editValue}
onChange={(e) => setEditValue(e.target.value)}
onBlur={(e) => handleSaveEdit(conv.id, e)}
onKeyDown={(e) => handleKeyDown(e, conv.id)}
onClick={(e) => e.stopPropagation()}
autoFocus
/>
) : (
<>
<div className="conversation-title">
{conv.title || 'New Conversation'}
</div>
<div className="conversation-meta">
{conv.message_count} messages
</div>
</>
)}
</div>
<div className="conversation-actions" onClick={(e) => e.stopPropagation()}>
{editingId !== conv.id && onRenameConversation && (
<button
className="conversation-rename-btn"
onClick={(e) => handleStartEdit(conv, e)}
title="Rename"
>
✏️
</button>
)}
{onDeleteConversation && (
<button
className="conversation-delete-btn"
onClick={(e) => onDeleteConversation(conv.id, e)}
title="Delete"
>
×
</button>
)}
</div>
</div>
))
)}
</div>
</div>
);
}

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.stage {
margin: 24px 0;
padding: 20px;
background: var(--bg-secondary);
border-radius: 8px;
border: 1px solid var(--border-color);
transition: background-color 0.2s, border-color 0.2s;
}
.stage-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 12px;
}
.stage-meta {
font-size: 12px;
color: var(--text-secondary);
font-weight: normal;
}
.stage-title {
margin: 0;
color: var(--text-primary);
font-size: 16px;
font-weight: 600;
}
.tabs {
display: flex;
gap: 8px;
margin-bottom: 16px;
flex-wrap: wrap;
}
.tab {
padding: 8px 16px;
background: var(--bg-primary);
border: 1px solid var(--border-color);
border-radius: 6px 6px 0 0;
color: var(--text-secondary);
cursor: pointer;
font-size: 14px;
transition: all 0.2s;
}
.tab:hover {
background: var(--hover-bg);
color: var(--text-primary);
border-color: #4a90e2;
}
.tab.active {
background: var(--bg-primary);
color: #4a90e2;
border-color: #4a90e2;
border-bottom-color: var(--bg-primary);
font-weight: 600;
}
.tab-content {
background: var(--bg-primary);
padding: 16px;
border-radius: 6px;
border: 1px solid var(--border-color);
transition: background-color 0.2s, border-color 0.2s;
}
.model-name {
color: var(--text-secondary);
font-size: 12px;
margin-bottom: 12px;
font-family: monospace;
}
.response-text {
color: var(--text-primary);
line-height: 1.6;
}

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import { useState } from 'react';
import ReactMarkdown from 'react-markdown';
import './Stage1.css';
export default function Stage1({ responses, metadata }) {
const [activeTab, setActiveTab] = useState(0);
if (!responses || responses.length === 0) {
return null;
}
const duration = metadata?.duration_seconds;
const successful = metadata?.successful_models?.length || 0;
const total = metadata?.total_models || 0;
return (
<div className="stage stage1">
<div className="stage-header">
<h3 className="stage-title">Stage 1: Individual Responses</h3>
{duration && (
<div className="stage-meta">
{duration}s | {successful}/{total} models
</div>
)}
</div>
<div className="tabs">
{responses.map((resp, index) => (
<button
key={index}
className={`tab ${activeTab === index ? 'active' : ''}`}
onClick={() => setActiveTab(index)}
>
{resp.model.split('/')[1] || resp.model}
</button>
))}
</div>
<div className="tab-content">
<div className="model-name">{responses[activeTab].model}</div>
<div className="response-text markdown-content">
<ReactMarkdown>{responses[activeTab].response}</ReactMarkdown>
</div>
</div>
</div>
);
}

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.stage-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 12px;
}
.stage-meta {
font-size: 12px;
color: var(--text-secondary);
font-weight: normal;
}
.stage2 {
background: var(--bg-secondary);
}
.stage2 h4 {
margin: 20px 0 8px 0;
color: var(--text-primary);
font-size: 14px;
font-weight: 600;
}
.stage2 h4:first-of-type {
margin-top: 0;
}
.stage-description {
margin: 0 0 12px 0;
color: var(--text-secondary);
font-size: 13px;
line-height: 1.5;
}
.aggregate-rankings {
background: var(--hover-bg-light);
padding: 16px;
border-radius: 8px;
margin-bottom: 20px;
border: 2px solid var(--border-hover);
transition: background-color 0.2s, border-color 0.2s;
}
.aggregate-rankings h4 {
margin: 0 0 12px 0;
color: #2a7ae2;
font-size: 15px;
}
.aggregate-list {
display: flex;
flex-direction: column;
gap: 8px;
}
.aggregate-item {
display: flex;
align-items: center;
gap: 12px;
padding: 10px;
background: var(--bg-primary);
border-radius: 6px;
border: 1px solid var(--border-hover);
transition: background-color 0.2s, border-color 0.2s;
}
.rank-position {
color: #2a7ae2;
font-weight: 700;
font-size: 16px;
min-width: 35px;
}
.rank-model {
flex: 1;
color: var(--text-primary);
font-family: monospace;
font-size: 14px;
font-weight: 500;
}
.rank-score {
color: var(--text-secondary);
font-size: 13px;
font-family: monospace;
}
.stage2 .tabs {
display: flex;
gap: 8px;
margin-bottom: 16px;
flex-wrap: wrap;
}
.stage2 .tab {
padding: 8px 16px;
background: var(--bg-primary);
border: 1px solid var(--border-color);
border-radius: 6px 6px 0 0;
color: var(--text-secondary);
cursor: pointer;
font-size: 14px;
transition: all 0.2s;
}
.stage2 .tab:hover {
background: var(--hover-bg);
color: var(--text-primary);
border-color: #4a90e2;
}
.stage2 .tab.active {
background: var(--bg-primary);
color: #4a90e2;
border-color: #4a90e2;
border-bottom-color: var(--bg-primary);
font-weight: 600;
}
.stage2 .tab-content {
background: var(--bg-primary);
padding: 16px;
border-radius: 6px;
border: 1px solid var(--border-color);
margin-bottom: 20px;
transition: background-color 0.2s, border-color 0.2s;
}
.ranking-model {
color: var(--text-secondary);
font-size: 12px;
font-family: monospace;
margin-bottom: 12px;
}
.ranking-content {
color: var(--text-primary);
line-height: 1.6;
font-size: 14px;
}
.parsed-ranking {
margin-top: 16px;
padding-top: 16px;
border-top: 2px solid var(--border-color);
transition: border-color 0.2s;
}
.parsed-ranking strong {
color: #2a7ae2;
font-size: 13px;
}
.parsed-ranking ol {
margin: 8px 0 0 0;
padding-left: 24px;
color: var(--text-primary);
}
.parsed-ranking li {
margin: 4px 0;
font-family: monospace;
font-size: 13px;
}
.rank-count {
color: var(--text-tertiary);
font-size: 12px;
}

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import { useState } from 'react';
import ReactMarkdown from 'react-markdown';
import './Stage2.css';
function deAnonymizeText(text, labelToModel) {
if (!labelToModel) return text;
let result = text;
// Replace each "Response X" with the actual model name
Object.entries(labelToModel).forEach(([label, model]) => {
const modelShortName = model.split('/')[1] || model;
result = result.replace(new RegExp(label, 'g'), `**${modelShortName}**`);
});
return result;
}
export default function Stage2({ rankings, labelToModel, aggregateRankings, metadata }) {
const [activeTab, setActiveTab] = useState(0);
if (!rankings || rankings.length === 0) {
return null;
}
const duration = metadata?.duration_seconds;
const successful = metadata?.successful_models?.length || 0;
const total = metadata?.total_models || 0;
return (
<div className="stage stage2">
<div className="stage-header">
<h3 className="stage-title">Stage 2: Peer Rankings</h3>
{duration && (
<div className="stage-meta">
{duration}s | {successful}/{total} models
</div>
)}
</div>
<h4>Raw Evaluations</h4>
<p className="stage-description">
Each model evaluated all responses (anonymized as Response A, B, C, etc.) and provided rankings.
Below, model names are shown in <strong>bold</strong> for readability, but the original evaluation used anonymous labels.
</p>
<div className="tabs">
{rankings.map((rank, index) => (
<button
key={index}
className={`tab ${activeTab === index ? 'active' : ''}`}
onClick={() => setActiveTab(index)}
>
{rank.model.split('/')[1] || rank.model}
</button>
))}
</div>
<div className="tab-content">
<div className="ranking-model">
{rankings[activeTab].model}
</div>
<div className="ranking-content markdown-content">
<ReactMarkdown>
{deAnonymizeText(rankings[activeTab].ranking, labelToModel)}
</ReactMarkdown>
</div>
{rankings[activeTab].parsed_ranking &&
rankings[activeTab].parsed_ranking.length > 0 && (
<div className="parsed-ranking">
<strong>Extracted Ranking:</strong>
<ol>
{rankings[activeTab].parsed_ranking.map((label, i) => (
<li key={i}>
{labelToModel && labelToModel[label]
? labelToModel[label].split('/')[1] || labelToModel[label]
: label}
</li>
))}
</ol>
</div>
)}
</div>
{aggregateRankings && aggregateRankings.length > 0 && (
<div className="aggregate-rankings">
<h4>Aggregate Rankings (Street Cred)</h4>
<p className="stage-description">
Combined results across all peer evaluations (lower score is better):
</p>
<div className="aggregate-list">
{aggregateRankings.map((agg, index) => (
<div key={index} className="aggregate-item">
<span className="rank-position">#{index + 1}</span>
<span className="rank-model">
{agg.model.split('/')[1] || agg.model}
</span>
<span className="rank-score">
Avg: {agg.average_rank.toFixed(2)}
</span>
<span className="rank-count">
({agg.rankings_count} votes)
</span>
</div>
))}
</div>
</div>
)}
</div>
);
}

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.stage-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 12px;
}
.stage-meta {
font-size: 12px;
color: var(--text-secondary);
font-weight: normal;
}
.total-duration {
margin-top: 12px;
padding-top: 12px;
border-top: 1px solid var(--border-light);
font-size: 13px;
color: var(--text-secondary);
}
.stage3 {
background: var(--bg-secondary);
border-color: var(--border-color);
}
.final-response {
background: var(--bg-primary);
padding: 20px;
border-radius: 6px;
border: 1px solid var(--border-color);
transition: background-color 0.2s, border-color 0.2s;
}
.chairman-label {
color: var(--text-primary);
font-size: 12px;
font-family: monospace;
margin-bottom: 12px;
font-weight: 600;
}
.final-text {
color: var(--text-primary);
line-height: 1.7;
font-size: 15px;
}

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import ReactMarkdown from 'react-markdown';
import './Stage3.css';
export default function Stage3({ finalResponse, metadata, totalDuration }) {
if (!finalResponse) {
return null;
}
const duration = metadata?.duration_seconds;
const model = metadata?.model || finalResponse.model;
return (
<div className="stage stage3">
<div className="stage-header">
<h3 className="stage-title">Stage 3: Final Council Answer</h3>
{duration && (
<div className="stage-meta">
{duration}s
</div>
)}
</div>
<div className="final-response">
<div className="chairman-label">
Chairman: {model.split('/')[1] || model}
</div>
<div className="final-text markdown-content">
<ReactMarkdown>{finalResponse.response}</ReactMarkdown>
</div>
{totalDuration && (
<div className="total-duration">
Total processing time: <strong>{totalDuration}s</strong>
</div>
)}
</div>
</div>
);
}

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:root {
font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', sans-serif;
line-height: 1.5;
font-weight: 400;
font-synthesis: none;
text-rendering: optimizeLegibility;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
/* Light theme (default) */
:root,
[data-theme="light"] {
--bg-primary: #ffffff;
--bg-secondary: #f8f8f8;
--bg-tertiary: #fbfbfb;
--bg-code: #f5f5f5;
--text-primary: #333333;
--text-secondary: #666666;
--text-tertiary: #999999;
--border-color: #e0e0e0;
--border-light: #eaeaea;
--hover-bg: #f5f5f5;
--hover-bg-light: #f0f7ff;
--border-hover: #d0e7ff;
}
/* Dark theme - improved contrast */
[data-theme="dark"] {
--bg-primary: #1a1a1a;
--bg-secondary: #252525;
--bg-tertiary: #2a2a2a;
--bg-code: #2d2d2d;
--text-primary: #ffffff;
--text-secondary: #e0e0e0;
--text-tertiary: #b0b0b0;
--border-color: #404040;
--border-light: #353535;
--hover-bg: #303030;
--hover-bg-light: #2a3a4a;
--border-hover: #4a5a6a;
--active-border: #5aa0f2;
--user-message-bg: #1e2a3a;
--user-message-border: #2a3a4a;
--stage-bg: #252525;
--stage-border: #404040;
--stage3-bg: #1e2a1e;
--stage3-border: #2a3a2a;
}
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
body {
margin: 0;
min-width: 320px;
min-height: 100vh;
background: var(--bg-secondary);
color: var(--text-primary);
transition: background-color 0.2s, color 0.2s;
}
#root {
height: 100vh;
width: 100vw;
overflow: hidden;
}
/* Global markdown styling */
.markdown-content {
padding: 12px;
}
.markdown-content p {
margin: 0 0 12px 0;
}
.markdown-content p:last-child {
margin-bottom: 0;
}
.markdown-content h1,
.markdown-content h2,
.markdown-content h3,
.markdown-content h4,
.markdown-content h5,
.markdown-content h6 {
margin: 16px 0 8px 0;
}
.markdown-content h1:first-child,
.markdown-content h2:first-child,
.markdown-content h3:first-child,
.markdown-content h4:first-child,
.markdown-content h5:first-child,
.markdown-content h6:first-child {
margin-top: 0;
}
.markdown-content ul,
.markdown-content ol {
margin: 0 0 12px 0;
padding-left: 24px;
}
.markdown-content li {
margin: 4px 0;
}
.markdown-content pre {
background: var(--bg-code);
padding: 12px;
border-radius: 4px;
overflow-x: auto;
margin: 0 0 12px 0;
}
.markdown-content code {
background: var(--bg-code);
padding: 2px 6px;
border-radius: 3px;
font-family: monospace;
font-size: 0.9em;
}
.markdown-content pre code {
background: none;
padding: 0;
}
.markdown-content blockquote {
margin: 0 0 12px 0;
padding-left: 16px;
border-left: 4px solid var(--border-color);
color: var(--text-secondary);
}

10
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import { StrictMode } from 'react'
import { createRoot } from 'react-dom/client'
import './index.css'
import App from './App.jsx'
createRoot(document.getElementById('root')).render(
<StrictMode>
<App />
</StrictMode>,
)

101
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import test from 'node:test';
import assert from 'node:assert/strict';
import { api } from '../src/api.js';
test('api.listDocuments calls correct endpoint', async () => {
const calls = [];
globalThis.fetch = async (url, options) => {
calls.push({ url: String(url), options });
return {
ok: true,
json: async () => [{ id: '1', filename: 'a.md', bytes: 3 }],
};
};
const out = await api.listDocuments('conv123');
assert.equal(calls.length, 1);
assert.match(calls[0].url, /\/api\/conversations\/conv123\/documents$/);
assert.deepEqual(out, [{ id: '1', filename: 'a.md', bytes: 3 }]);
});
test('api.uploadDocument uses multipart form and POST', async () => {
const calls = [];
globalThis.fetch = async (url, options) => {
calls.push({ url: String(url), options });
return {
ok: true,
json: async () => ({ id: 'doc1', filename: 'x.md', bytes: 10 }),
};
};
// Node 18 doesn't provide a global File, but it does provide Blob.
// Our api.uploadDocument supports Blob + filename.
const file = new Blob(['hello'], { type: 'text/markdown' });
file.name = 'x.md';
const out = await api.uploadDocument('conv999', file);
assert.equal(calls.length, 1);
assert.match(calls[0].url, /\/api\/conversations\/conv999\/documents$/);
assert.equal(calls[0].options.method, 'POST');
assert.ok(calls[0].options.body instanceof FormData);
assert.equal(out.id, 'doc1');
});
test('api.uploadDocuments uses multipart form and POST', async () => {
const calls = [];
globalThis.fetch = async (url, options) => {
calls.push({ url: String(url), options });
return {
ok: true,
json: async () => ({ uploaded: [{ id: 'a' }, { id: 'b' }] }),
};
};
const a = new Blob(['one'], { type: 'text/markdown' });
a.name = 'a.md';
const b = new Blob(['two'], { type: 'text/markdown' });
b.name = 'b.md';
const out = await api.uploadDocuments('conv999', [a, b]);
assert.equal(calls.length, 1);
assert.match(calls[0].url, /\/api\/conversations\/conv999\/documents$/);
assert.equal(calls[0].options.method, 'POST');
assert.ok(calls[0].options.body instanceof FormData);
assert.equal(out.uploaded.length, 2);
});
test('api.getDocument calls correct endpoint', async () => {
const calls = [];
globalThis.fetch = async (url) => {
calls.push({ url: String(url) });
return {
ok: true,
json: async () => ({ id: 'd1', content: '# hi' }),
};
};
const out = await api.getDocument('c1', 'd1');
assert.equal(calls.length, 1);
assert.match(calls[0].url, /\/api\/conversations\/c1\/documents\/d1$/);
assert.equal(out.content, '# hi');
});
test('api.deleteDocument uses DELETE', async () => {
const calls = [];
globalThis.fetch = async (url, options) => {
calls.push({ url: String(url), options });
return { ok: true, json: async () => ({ ok: true }) };
};
const out = await api.deleteDocument('c1', 'd1');
assert.equal(calls.length, 1);
assert.match(calls[0].url, /\/api\/conversations\/c1\/documents\/d1$/);
assert.equal(calls[0].options.method, 'DELETE');
assert.equal(out.ok, true);
});

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import test from 'node:test';
import assert from 'node:assert/strict';
import fs from 'node:fs';
import path from 'node:path';
// Node 18 can't import .jsx without a loader; do a lightweight source-based test instead.
test('ChatInterface includes Documents UI and always-visible input form', () => {
const filePath = path.resolve(
process.cwd(),
'src/components/ChatInterface.jsx'
);
const src = fs.readFileSync(filePath, 'utf8');
assert.ok(src.includes('className="docs-bar"'));
assert.ok(src.includes('Upload .md'));
assert.ok(src.includes('className="docs-list"'));
assert.ok(src.includes('Delete'));
assert.ok(src.includes('doc-preview'));
assert.ok(src.includes('className="input-form"'));
assert.ok(src.includes('className="message-input"'));
});

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import { defineConfig } from 'vite'
import react from '@vitejs/plugin-react'
// https://vite.dev/config/
export default defineConfig({
plugins: [react()],
})

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pyproject.toml Normal file
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[project]
name = "llm-council"
version = "0.1.0"
description = "Your LLM Council"
readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"fastapi>=0.115.0",
"uvicorn[standard]>=0.32.0",
"python-dotenv>=1.0.0",
"httpx>=0.27.0",
"pydantic>=2.9.0",
"python-multipart>=0.0.9",
]

24
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#!/bin/bash
# Check if firewall might be blocking the connection
GPU_VM="10.0.30.63"
echo "=== Firewall Check ==="
echo ""
echo "The fact that Ollama shows ':::11434' means it's listening on all interfaces."
echo "If curl still times out, it's likely a firewall issue."
echo ""
echo "On the GPU VM, check firewall:"
echo " # Check if firewall is running:"
echo " sudo ufw status"
echo " # OR"
echo " sudo iptables -L -n | grep 11434"
echo ""
echo "If firewall is blocking, allow the port:"
echo " sudo ufw allow 11434/tcp"
echo " # OR for iptables:"
echo " sudo iptables -A INPUT -p tcp --dport 11434 -j ACCEPT"
echo ""
echo "Testing connection from local machine..."
timeout 3 curl -v http://$GPU_VM:11434/api/tags 2>&1 | grep -E "Connected|timeout|refused|Connection" | head -3

60
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#!/bin/bash
# Check if Ollama models still exist on GPU VM
# Run this ON THE GPU VM
echo "=== Checking Ollama Models ==="
echo ""
# Check Ollama's model storage locations
echo "1. Checking Ollama API for models:"
curl -s http://localhost:11434/api/tags | python3 -m json.tool 2>/dev/null | grep -E '"name"|"model"' | head -10
echo ""
echo "2. Checking common model storage locations:"
echo " ~/.ollama/models:"
if [ -d ~/.ollama/models ]; then
du -sh ~/.ollama/models
ls -lh ~/.ollama/models | head -5
else
echo " ✗ Not found"
fi
echo ""
echo " /usr/share/ollama/models:"
if [ -d /usr/share/ollama/models ]; then
du -sh /usr/share/ollama/models
ls -lh /usr/share/ollama/models | head -5
else
echo " ✗ Not found"
fi
echo ""
echo " /var/lib/ollama/models:"
if [ -d /var/lib/ollama/models ]; then
du -sh /var/lib/ollama/models
ls -lh /var/lib/ollama/models | head -5
else
echo " ✗ Not found"
fi
echo ""
echo "3. Finding Ollama data directory:"
if command -v ollama > /dev/null; then
ollama show 2>&1 | head -5
fi
echo ""
echo "4. Checking systemd service for OLLAMA_MODELS path:"
systemctl show ollama | grep -i model || echo " No OLLAMA_MODELS env var set"
echo ""
echo "=== What we did ==="
echo "We only created: /etc/systemd/system/ollama.service.d/override.conf"
echo "This file only sets: OLLAMA_HOST=0.0.0.0:11434"
echo "It does NOT delete models."
echo ""
echo "If models are missing, they might be:"
echo " 1. In a different location (check above)"
echo " 2. Ollama needs to be restarted to see them"
echo " 3. Models were deleted separately (not by our script)"

40
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#!/bin/bash
# Script to configure Ollama on GPU VM to listen on all interfaces
# Run this on the GPU VM (10.0.30.63)
echo "Configuring Ollama to listen on all interfaces..."
# Method 1: Set environment variable (temporary, until reboot)
export OLLAMA_HOST=0.0.0.0:11434
echo "✓ Set OLLAMA_HOST=0.0.0.0:11434 (temporary)"
# Method 2: Create systemd override (permanent)
echo ""
echo "Creating systemd override for permanent configuration..."
sudo mkdir -p /etc/systemd/system/ollama.service.d
sudo tee /etc/systemd/system/ollama.service.d/override.conf > /dev/null <<EOF
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
EOF
echo "✓ Created override file: /etc/systemd/system/ollama.service.d/override.conf"
# Reload systemd and restart Ollama
echo ""
echo "Reloading systemd and restarting Ollama..."
sudo systemctl daemon-reload
sudo systemctl restart ollama
echo ""
echo "✓ Ollama restarted"
echo ""
echo "Verifying configuration..."
sleep 2
curl -s http://localhost:11434/api/tags | head -c 200
echo ""
echo ""
echo "✓ Configuration complete!"
echo ""
echo "You can now test from your local machine:"
echo " curl http://10.0.30.63:11434/api/tags"

57
scripts/diagnose_gpu_vm.sh Executable file
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#!/bin/bash
# Quick diagnostic script for GPU VM connection
GPU_VM="10.0.30.63"
echo "=== GPU VM Connection Diagnostics ==="
echo ""
echo "1. Testing basic connectivity..."
if ping -c 1 -W 2 $GPU_VM > /dev/null 2>&1; then
echo " ✓ GPU VM is reachable"
else
echo " ✗ Cannot reach GPU VM - check network/firewall"
exit 1
fi
echo ""
echo "2. Testing port 11434 (Ollama default)..."
if timeout 3 curl -s http://$GPU_VM:11434/api/tags > /dev/null 2>&1; then
echo " ✓ Port 11434 is open and responding"
echo " Models available:"
curl -s http://$GPU_VM:11434/api/tags | python3 -m json.tool 2>/dev/null | grep -E '"name"|"model"' | head -5
else
echo " ✗ Port 11434 not responding"
echo " Error details:"
timeout 3 curl -v http://$GPU_VM:11434/api/tags 2>&1 | grep -E "Connection|timeout|refused" | head -3
fi
echo ""
echo "3. Testing port 8000 (alternative)..."
if timeout 3 curl -s http://$GPU_VM:8000/v1/models > /dev/null 2>&1; then
echo " ✓ Port 8000 is open and responding"
else
echo " ✗ Port 8000 not responding"
fi
echo ""
echo "4. Checking your .env configuration..."
if [ -f .env ]; then
echo " OPENAI_COMPAT_BASE_URL: $(grep OPENAI_COMPAT_BASE_URL .env | grep -v '^#' | cut -d'=' -f2)"
echo " USE_LOCAL_OLLAMA: $(grep USE_LOCAL_OLLAMA .env | grep -v '^#' | cut -d'=' -f2)"
else
echo " ✗ .env file not found"
fi
echo ""
echo "=== Recommendations ==="
echo ""
echo "If port 11434 is not working:"
echo " 1. SSH to GPU VM: ssh root@$GPU_VM"
echo " 2. Check if Ollama is running: systemctl status ollama"
echo " 3. Check what port Ollama is listening on: netstat -tlnp | grep ollama"
echo " 4. If only listening on 127.0.0.1, configure it to listen on 0.0.0.0"
echo ""
echo "If you need to use a different port, update .env:"
echo " OPENAI_COMPAT_BASE_URL=http://$GPU_VM:PORT"

59
scripts/find_ollama_models.sh Executable file
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#!/bin/bash
# Find and verify Ollama models on GPU VM
# Run this ON THE GPU VM
echo "=== Finding Ollama Models ==="
echo ""
echo "1. Check what Ollama API reports:"
echo " Running: curl http://localhost:11434/api/tags"
curl -s http://localhost:11434/api/tags | python3 -m json.tool 2>/dev/null || curl -s http://localhost:11434/api/tags
echo ""
echo ""
echo "2. Find Ollama data directory:"
echo " Checking common locations..."
# Check for OLLAMA_MODELS env var
if [ -n "$OLLAMA_MODELS" ]; then
echo " OLLAMA_MODELS env var: $OLLAMA_MODELS"
if [ -d "$OLLAMA_MODELS" ]; then
echo " ✓ Found! Size: $(du -sh "$OLLAMA_MODELS" 2>/dev/null | cut -f1)"
echo " Models:"
ls -lh "$OLLAMA_MODELS" | head -10
fi
fi
# Check common locations
for dir in ~/.ollama/models ~/.ollama /usr/share/ollama/models /usr/share/ollama /var/lib/ollama/models /var/lib/ollama; do
if [ -d "$dir" ]; then
echo " Found: $dir"
echo " Size: $(du -sh "$dir" 2>/dev/null | cut -f1)"
if [ -d "$dir/models" ]; then
echo " Models in subdirectory:"
ls -lh "$dir/models" 2>/dev/null | head -5
fi
find "$dir" -name "*.gguf" -o -name "*.bin" 2>/dev/null | head -5
fi
done
echo ""
echo "3. Check Ollama process environment:"
sudo cat /proc/$(pgrep -f ollama | head -1)/environ 2>/dev/null | tr '\0' '\n' | grep -i model || echo " No OLLAMA_MODELS in process env"
echo ""
echo "4. Check systemd service environment:"
systemctl show ollama | grep -i environment
echo ""
echo "=== If models are missing ==="
echo "They might be in a different location. Ollama stores models in:"
echo " - Default: ~/.ollama/models (or /usr/share/ollama/models)"
echo " - Or wherever OLLAMA_MODELS env var points"
echo ""
echo "To re-download models:"
echo " ollama pull qwen2:latest"
echo " ollama pull qwen2.5:14b"
echo " ollama pull llama3.1:8b"
echo " ollama pull qwen2.5:7b"

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scripts/fix_firewall_gpu_vm.sh Executable file
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#!/bin/bash
# Run this ON THE GPU VM to fix firewall
echo "=== Fixing Firewall for Ollama ==="
echo ""
# Check what firewall is running
if command -v ufw > /dev/null 2>&1; then
echo "Detected UFW firewall"
echo "Current status:"
sudo ufw status | head -5
echo ""
echo "Allowing port 11434..."
sudo ufw allow 11434/tcp
echo "✓ Port 11434 allowed"
elif command -v firewall-cmd > /dev/null 2>&1; then
echo "Detected firewalld"
sudo firewall-cmd --permanent --add-port=11434/tcp
sudo firewall-cmd --reload
echo "✓ Port 11434 allowed"
else
echo "Checking iptables..."
if sudo iptables -L -n | grep -q 11434; then
echo "Found iptables rules for 11434"
else
echo "Adding iptables rule..."
sudo iptables -A INPUT -p tcp --dport 11434 -j ACCEPT
echo "✓ Rule added (may need to save: sudo iptables-save)"
fi
fi
echo ""
echo "Verifying Ollama is accessible..."
sleep 1
curl -s http://localhost:11434/api/tags | head -c 100
echo ""
echo ""
echo "✓ Done! Test from your local machine:"
echo " curl http://10.0.30.63:11434/api/tags"

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scripts/fix_ollama_remote.sh Executable file
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#!/bin/bash
# Run this ON THE GPU VM (10.0.30.63) to fix Ollama remote access
echo "=== Fixing Ollama Remote Access ==="
echo ""
# Check if Ollama is running
if ! systemctl is-active --quiet ollama; then
echo "✗ Ollama is not running. Starting it..."
sudo systemctl start ollama
sleep 2
fi
echo "✓ Ollama is running"
echo ""
# Check what it's listening on
echo "Current Ollama listening status:"
sudo netstat -tlnp 2>/dev/null | grep 11434 || ss -tlnp 2>/dev/null | grep 11434
echo ""
# Create systemd override
echo "Creating systemd override to listen on all interfaces..."
sudo mkdir -p /etc/systemd/system/ollama.service.d
sudo tee /etc/systemd/system/ollama.service.d/override.conf > /dev/null <<'EOF'
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
EOF
echo "✓ Override file created"
echo ""
# Reload and restart
echo "Reloading systemd and restarting Ollama..."
sudo systemctl daemon-reload
sudo systemctl restart ollama
sleep 3
echo "✓ Ollama restarted"
echo ""
# Verify
echo "Verifying configuration..."
if sudo netstat -tlnp 2>/dev/null | grep -q "0.0.0.0:11434" || sudo ss -tlnp 2>/dev/null | grep -q "0.0.0.0:11434"; then
echo "✓ SUCCESS! Ollama is now listening on 0.0.0.0:11434"
echo ""
echo "Test from your local machine:"
echo " curl http://10.0.30.63:11434/api/tags"
else
echo "✗ Still not listening on 0.0.0.0 - checking status..."
sudo systemctl status ollama --no-pager | head -10
echo ""
echo "Try checking Ollama logs:"
echo " sudo journalctl -u ollama -n 20"
fi

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scripts/fix_ollama_storage.sh Executable file
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#!/bin/bash
# Configure Ollama to use /mnt/data for model storage
# Run this ON THE GPU VM
echo "=== Fixing Ollama Storage Location ==="
echo ""
# Check current disk usage
echo "Current disk usage:"
df -h | grep -E "Filesystem|/dev/sda"
echo ""
# Create models directory on /mnt/data
echo "Creating Ollama models directory on /mnt/data..."
sudo mkdir -p /mnt/data/ollama/models
sudo chown -R ollama:ollama /mnt/data/ollama 2>/dev/null || sudo chown -R $(whoami):$(whoami) /mnt/data/ollama
echo "✓ Directory created: /mnt/data/ollama/models"
echo ""
# Check if there are existing models to move
if [ -d ~/.ollama/models ] && [ "$(ls -A ~/.ollama/models 2>/dev/null)" ]; then
echo "Found existing models in ~/.ollama/models"
echo "Moving to /mnt/data/ollama/models..."
sudo mv ~/.ollama/models/* /mnt/data/ollama/models/ 2>/dev/null
echo "✓ Models moved"
elif [ -d /usr/share/ollama/models ] && [ "$(ls -A /usr/share/ollama/models 2>/dev/null)" ]; then
echo "Found existing models in /usr/share/ollama/models"
echo "Moving to /mnt/data/ollama/models..."
sudo mv /usr/share/ollama/models/* /mnt/data/ollama/models/ 2>/dev/null
echo "✓ Models moved"
else
echo "No existing models found to move"
fi
echo ""
# Update systemd service to use new location
echo "Updating systemd service configuration..."
sudo mkdir -p /etc/systemd/system/ollama.service.d
# Check if override.conf exists and update it, or create new
if [ -f /etc/systemd/system/ollama.service.d/override.conf ]; then
echo "Updating existing override.conf..."
# Add OLLAMA_MODELS if not already there
if ! grep -q "OLLAMA_MODELS" /etc/systemd/system/ollama.service.d/override.conf; then
sudo sed -i '/\[Service\]/a Environment="OLLAMA_MODELS=/mnt/data/ollama/models"' /etc/systemd/system/ollama.service.d/override.conf
else
sudo sed -i 's|OLLAMA_MODELS=.*|OLLAMA_MODELS=/mnt/data/ollama/models|' /etc/systemd/system/ollama.service.d/override.conf
fi
else
echo "Creating new override.conf..."
sudo tee /etc/systemd/system/ollama.service.d/override.conf > /dev/null <<EOF
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
Environment="OLLAMA_MODELS=/mnt/data/ollama/models"
EOF
fi
echo "✓ Systemd configuration updated"
echo ""
# Reload and restart
echo "Reloading systemd and restarting Ollama..."
sudo systemctl daemon-reload
sudo systemctl restart ollama
sleep 3
echo "✓ Ollama restarted with new storage location"
echo ""
# Verify
echo "Verifying configuration:"
echo " Storage location: /mnt/data/ollama/models"
echo " Disk space available:"
df -h /mnt/data | tail -1
echo ""
echo " Checking if Ollama is running:"
systemctl is-active ollama && echo " ✓ Ollama is running" || echo " ✗ Ollama is not running"
echo ""
echo "=== Done! ==="
echo "You can now pull models:"
echo " ollama pull qwen2:latest"
echo " ollama pull qwen2.5:14b"
echo " ollama pull llama3.1:8b"
echo " ollama pull qwen2.5:7b"

54
scripts/test_gpu_vm.py Executable file
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#!/usr/bin/env python3
"""Quick test script to check GPU VM connection."""
import asyncio
import sys
sys.path.insert(0, '.')
from backend.llm_client import list_models
from backend.config import OPENAI_COMPAT_BASE_URL, COUNCIL_MODELS, CHAIRMAN_MODEL
async def test_connection():
print(f"Testing connection to: {OPENAI_COMPAT_BASE_URL}")
print(f"Configured council models: {COUNCIL_MODELS}")
print(f"Chairman model: {CHAIRMAN_MODEL}")
print("-" * 60)
try:
models = await list_models()
if models is None:
print("✗ Unable to list models (connection error, timeout, or incompatible endpoint).")
print("")
print("Next checks:")
print(f" - curl {OPENAI_COMPAT_BASE_URL.rstrip('/')}/api/tags")
print(f" - curl {OPENAI_COMPAT_BASE_URL.rstrip('/')}/v1/models")
print("")
print("If you're using Ollama remotely, the port is usually 11434.")
return
if models:
print(f"✓ Connection successful!")
print(f"Found {len(models)} available models:\n")
for model in models:
marker = "✓" if model in COUNCIL_MODELS else " "
chairman_marker = " (CHAIRMAN)" if model == CHAIRMAN_MODEL else ""
print(f" {marker} {model}{chairman_marker}")
print("\n" + "-" * 60)
missing = [m for m in COUNCIL_MODELS if m not in models]
if missing:
print(f"⚠ Warning: {len(missing)} configured models not found:")
for m in missing:
print(f" - {m}")
else:
print("✓ All configured council models are available!")
else:
print("✗ Connected, but the server returned an empty model list.")
print(" This is unusual for Ollama; double-check the base URL/port and server.")
except Exception as e:
print(f"✗ Connection failed: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(test_connection())

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scripts/test_gpu_vm_detailed.py Normal file
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#!/usr/bin/env python3
"""Detailed test script to check GPU VM connection and endpoints."""
import asyncio
import sys
sys.path.insert(0, '.')
import httpx
from backend.config import OPENAI_COMPAT_BASE_URL
async def test_endpoints():
base_url = "http://10.0.30.63"
ports = [8000, 11434]
endpoints = [
"/v1/models",
"/api/tags",
"/api/version",
"/",
]
print("Testing GPU VM connection...")
print("=" * 60)
for port in ports:
print(f"\nTesting port {port}:")
print("-" * 60)
for endpoint in endpoints:
url = f"{base_url}:{port}{endpoint}"
try:
async with httpx.AsyncClient(timeout=5.0) as client:
resp = await client.get(url)
print(f" {endpoint:20} -> Status: {resp.status_code}")
if resp.status_code == 200:
try:
data = resp.json()
if isinstance(data, dict):
if 'data' in data:
models = data['data']
print(f" Found {len(models)} models via /v1/models")
if models:
print(f" First model: {models[0].get('id', models[0])}")
elif 'models' in data:
models = data['models']
print(f" Found {len(models)} models via /api/tags")
if models:
print(f" First model: {models[0].get('name', models[0])}")
else:
print(f" Response keys: {list(data.keys())[:5]}")
elif isinstance(data, list):
print(f" Found {len(data)} items")
except:
print(f" Response (first 200 chars): {resp.text[:200]}")
except httpx.TimeoutException:
print(f" {endpoint:20} -> Timeout")
except httpx.ConnectError:
print(f" {endpoint:20} -> Connection refused")
except Exception as e:
print(f" {endpoint:20} -> Error: {type(e).__name__}")
if __name__ == "__main__":
asyncio.run(test_endpoints())

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scripts/test_model_query.py Normal file
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#!/usr/bin/env python3
"""Test that we can actually query a model and get a response."""
import asyncio
import sys
sys.path.insert(0, '.')
from backend.llm_client import query_model
from backend.config import COUNCIL_MODELS
async def test_query():
"""Test querying one of the council models."""
if not COUNCIL_MODELS:
print("✗ No council models configured")
return False
test_model = COUNCIL_MODELS[0]
print(f"Testing query to model: {test_model}")
print("-" * 60)
try:
response = await query_model(
model=test_model,
messages=[{"role": "user", "content": "Say 'Hello, GPU Ollama is working!' in one sentence."}],
max_tokens_override=50, # Short response for quick test
timeout=30.0
)
if response and response.get('content'):
content = response['content'].strip()
print(f"✓ Query successful!")
print(f"\nResponse:")
print(f" {content}")
return True
else:
print(f"✗ Query returned no content")
print(f"Response: {response}")
return False
except Exception as e:
print(f"✗ Query failed: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
return False
if __name__ == "__main__":
success = asyncio.run(test_query())
sys.exit(0 if success else 1)

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scripts/test_model_timeout.py Normal file
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#!/usr/bin/env python3
"""Test script to diagnose model timeout issues."""
import asyncio
import time
import httpx
from backend.config import OPENAI_COMPAT_BASE_URL, LLM_TIMEOUT_SECONDS, DEBUG
async def test_model(model: str, max_tokens: int = 10):
"""Test a single model query."""
print(f"\n{'='*60}")
print(f"Testing model: {model}")
print(f"Timeout: {LLM_TIMEOUT_SECONDS}s")
print(f"Base URL: {OPENAI_COMPAT_BASE_URL}")
print(f"{'='*60}")
url = f"{OPENAI_COMPAT_BASE_URL}/v1/chat/completions"
payload = {
"model": model,
"messages": [{"role": "user", "content": "Say hello"}],
"max_tokens": max_tokens
}
start_time = time.time()
try:
async with httpx.AsyncClient(timeout=LLM_TIMEOUT_SECONDS) as client:
print(f"[{time.time() - start_time:.1f}s] Sending request...")
response = await client.post(url, json=payload)
elapsed = time.time() - start_time
print(f"[{elapsed:.1f}s] Response received: Status {response.status_code}")
if response.status_code == 200:
data = response.json()
content = data.get("choices", [{}])[0].get("message", {}).get("content", "")
print(f"✓ Success! Response: {content[:100]}")
return True
else:
print(f"✗ Error: {response.status_code}")
print(f" Response: {response.text[:200]}")
return False
except httpx.TimeoutException:
elapsed = time.time() - start_time
print(f"✗ Timeout after {elapsed:.1f}s (limit was {LLM_TIMEOUT_SECONDS}s)")
return False
except Exception as e:
elapsed = time.time() - start_time
print(f"✗ Error after {elapsed:.1f}s: {type(e).__name__}: {e}")
return False
async def main():
models = ["llama3.2:1b", "qwen2.5:0.5b", "gemma2:2b"]
print("Testing models sequentially to diagnose timeout issues...")
print(f"Current timeout setting: {LLM_TIMEOUT_SECONDS}s")
results = {}
for model in models:
results[model] = await test_model(model)
# Small delay between tests
await asyncio.sleep(1)
print(f"\n{'='*60}")
print("Summary:")
for model, success in results.items():
status = "✓ PASS" if success else "✗ FAIL"
print(f" {model}: {status}")
print(f"{'='*60}")
if __name__ == "__main__":
asyncio.run(main())

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scripts/test_ollama_direct.py Normal file
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#!/usr/bin/env python3
"""Test direct connection to Ollama on GPU VM."""
import asyncio
import httpx
async def test():
base = "http://10.0.30.63:11434"
urls = [
f"{base}/v1/models",
f"{base}/api/tags",
]
for url in urls:
print(f"\nTesting: {url}")
try:
async with httpx.AsyncClient(timeout=5.0) as client:
resp = await client.get(url)
print(f" Status: {resp.status_code}")
if resp.status_code == 200:
data = resp.json()
print(f" Keys: {list(data.keys())}")
if 'models' in data:
models = [m.get('name', m.get('model', m)) for m in data['models']]
print(f" Found {len(models)} models: {models}")
elif 'data' in data:
models = [m.get('id', m) for m in data['data']]
print(f" Found {len(models)} models: {models}")
except httpx.TimeoutException:
print(f" ✗ Timeout - Ollama may only be listening on localhost")
except Exception as e:
print(f" ✗ Error: {e}")
asyncio.run(test())

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scripts/test_setup.py Executable file
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#!/usr/bin/env python3
"""Setup a test conversation with optional message and documents."""
import os
import sys
import httpx
import time
from datetime import datetime
from pathlib import Path
from dotenv import load_dotenv
from urllib.parse import quote
# Load .env file if it exists
load_dotenv()
API_BASE = "http://localhost:8001"
def create_test_conversation():
"""Create a new conversation with today's date/time as title."""
now = datetime.now()
title = now.strftime("%Y-%m-%d %H:%M:%S")
with httpx.Client(timeout=10.0) as client:
# Create conversation
response = client.post(f"{API_BASE}/api/conversations", json={})
if response.status_code != 200:
print(f"Error creating conversation: {response.text}", file=sys.stderr)
sys.exit(1)
conv = response.json()
conv_id = conv["id"]
# Update title
response = client.patch(
f"{API_BASE}/api/conversations/{conv_id}/title",
json={"title": title}
)
if response.status_code != 200:
print(f"Warning: Could not set title: {response.text}", file=sys.stderr)
print(f"Created conversation: {conv_id}")
print(f"Title: {title}")
return conv_id
def upload_document(conv_id, filepath):
"""Upload a document to a conversation."""
path = Path(filepath)
if not path.exists():
print(f"Warning: File not found: {filepath}", file=sys.stderr)
return False
with httpx.Client(timeout=30.0) as client:
with open(path, 'rb') as f:
files = {'file': (path.name, f.read(), 'text/markdown')}
response = client.post(
f"{API_BASE}/api/conversations/{conv_id}/documents",
files=files
)
if response.status_code != 200:
print(f"Warning: Could not upload {filepath}: {response.text}", file=sys.stderr)
return False
print(f"Uploaded: {path.name}")
return True
def send_message(conv_id, message):
"""Send a message to a conversation."""
# Use a very long timeout since the council process can take several minutes
# Default to 10 minutes, but allow override via env var
timeout_seconds = float(os.getenv("TEST_MESSAGE_TIMEOUT_SECONDS", "600.0"))
print(f"Sending message (this may take several minutes, timeout: {timeout_seconds}s)...")
with httpx.Client(timeout=timeout_seconds) as client:
try:
response = client.post(
f"{API_BASE}/api/conversations/{conv_id}/message",
json={"content": message}
)
if response.status_code != 200:
print(f"Warning: Could not send message: {response.text}", file=sys.stderr)
return False
print(f"✓ Message sent and processed: {message[:50]}...")
return True
except httpx.ReadTimeout:
print(f"Error: Request timed out after {timeout_seconds}s", file=sys.stderr)
print("The council process is still running. You can check the conversation in the UI.", file=sys.stderr)
print(f"Conversation ID: {conv_id}", file=sys.stderr)
return False
except httpx.RequestError as e:
print(f"Error sending message: {e}", file=sys.stderr)
return False
def main():
# Wait for backend to be ready
max_retries = 10
for i in range(max_retries):
try:
with httpx.Client(timeout=1.0) as client:
response = client.get(f"{API_BASE}/")
if response.status_code == 200:
break
except httpx.RequestError:
if i < max_retries - 1:
time.sleep(1)
else:
print(f"Error: Backend not available at {API_BASE}", file=sys.stderr)
print("Make sure the backend is running: uv run python -m backend.main", file=sys.stderr)
sys.exit(1)
# Create conversation
conv_id = create_test_conversation()
# Upload documents if specified
test_docs = os.getenv("TEST_DOCS", "")
if test_docs:
doc_paths = [p.strip() for p in test_docs.split(",") if p.strip()]
for doc_path in doc_paths:
upload_document(conv_id, doc_path)
# Note: TEST_MESSAGE is NOT sent automatically
# It's provided here for reference - user should type it in the UI
test_message = os.getenv("TEST_MESSAGE", "")
open_url = f"http://localhost:5173/?conversation={conv_id}"
if test_message:
open_url += f"&message={quote(test_message)}"
print(f"\n✓ Conversation created: {conv_id}")
print(f"CONVERSATION_ID={conv_id}") # For Makefile to parse
print(f"OPEN_URL={open_url}") # For Makefile to parse
print(f"Open in browser: {open_url}")
if test_message:
print(f"\n💡 Pre-filled message (copy/paste into input):")
print(f" {test_message}")
return conv_id
if __name__ == "__main__":
main()

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start.sh Executable file
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#!/bin/bash
# LLM Council - Start script
echo "Starting LLM Council..."
echo ""
# Start backend
echo "Starting backend on http://localhost:8001..."
uv run python -m backend.main &
BACKEND_PID=$!
# Wait a bit for backend to start
sleep 2
# Start frontend
echo "Starting frontend on http://localhost:5173..."
cd frontend
npm run dev &
FRONTEND_PID=$!
echo ""
echo "✓ LLM Council is running!"
echo " Backend: http://localhost:8001"
echo " Frontend: http://localhost:5173"
echo ""
echo "Press Ctrl+C to stop both servers"
# Wait for Ctrl+C
trap "kill $BACKEND_PID $FRONTEND_PID 2>/dev/null; exit" SIGINT SIGTERM
wait

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# The Digital Garden
Once upon a time, in a world where code grew like vines, there lived a developer named Alex who discovered something magical in their repository.
Alex had been debugging a particularly stubborn bug for three days straight. The error messages were cryptic, the stack traces were confusing, and coffee had stopped working its usual magic.
## The Discovery
On the fourth morning, while scrolling through documentation that made less sense than the bug itself, Alex noticed something strange. Every time they ran their tests, a new file appeared in the `mysteries/` directory—a file that hadn't been there before.
The file was called `clue.md` and it contained only this:
> "Look not in the code, but between the lines."
## The Revelation
At first, Alex thought it was a prank from a coworker or a stray script. But as the days passed, the file began to change. New clues appeared, each more cryptic than the last.
It wasn't until Alex uploaded one of these files to their LLM Council that everything clicked. The council of AI models analyzed the patterns, the timing, the syntax. They saw connections Alex had missed.
## The Solution
The bug wasn't in Alex's code at all. It was in the dependencies, hidden in a nested module that updated itself every time the tests ran. The mysterious files were breadcrumbs left by a self-modifying dependency that was trying to communicate.
Alex fixed the issue, updated the dependencies, and the mysterious files stopped appearing. But they kept one—the first `clue.md`—as a reminder that sometimes the solution lies not in what you're looking for, but in what finds you.
---
*The end. Or is it just the beginning?*

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#author: allan & Burridge
#text: taboos
## Definition
- Taboo = a “proscription of behaviour that affects everyday life” (p. 1).
- Taboo arises where behaviors “can cause discomfort, harm or injury” (p. 1), encompassing both physical and metaphysical risks, including contact with sacred or dangerous persons or objects.
- The concept extends far beyond ritual acts or special circumstances.
- Risk can be metaphysical (contaminating soul), moral, or physical (exposure to disease or power), with possible “contamination” of others (p. 1).
- Can be protective: rules against incest, waste management, food taboos, and avoidance speech styles are evolutionarily or functionally sensible, though motivation may become obscured as rituals develop (p. 8).
### Domains (p. 1)
- bodies and their effluvia (sweat, snot, faeces, menstrual fluid)
- organs and acts of sex
- micturition and defecation; disease
- death and killing
- naming, addressing, touching, and viewing persons and sacred beings
- food gathering, preparation, and consumption
### Characteristics
1. not static: relative, context-dependent, historically shifting
2. multi-functional: protects, polices, empowers, and creates
3. systemic: operates across language, ritual, law, social identity
4. generative: produces linguistic creativity and cultural cohesion even as it restricts
### Key Mechanisms
1. orthophemism (straight talking)
2. euphemism (sweet talking)
3. dysphemism (speaking offensively)
## Informal and Formal Social & Legal Control
- Taboo is imposed externally: constraints “imposed by someone or some physical or metaphysical force that the individual believes has authority or power over them—the law, the gods, the society in which one lives, even proprioceptions” (p. 7–8).
- Informal vs. formal control: Many proscribed acts are maintained “by unwritten conventions governing behavioural standards” in families, teams, or workplaces, and only some become written law (pp. 7–8).
- Consequences span biological, social, supernatural, and legal domains.
- “Infractions ... can lead to illness or death, as well as to the lesser penalties of corporal punishment, incarceration, social ostracism or mere disapproval” (p. 1).
- Examples: death for incest in Hawaii, stoning for adultery under Sharia, execution for murder (p. 5), retrospective social blame (“bad karma,” “bad luck”) (p. 6).
- Consequences can follow accidental or intentional actions.
- “Even an unintended contravention ... risks condemnation and censure; generally, people can and do avoid tabooed behaviour unless they intend to violate a taboo” (p. 1).
## Everyday Censoring vs. Institutional Censorship
- Everyday censoring: “People constantly censor the language they use (we differentiate this from the institutionalized imposition of censorship)” (p. 1).
- “By default we are polite, euphemistic, orthophemistic and inoffensive and we censor our language use to eschew tabooed topics in pursuit of well-being for ourselves and for others” (p. 1).
- Institutional censorship: “Censorship is the suppression or prohibition of speech or writing that is condemned as subversive of the common good” (p. 13); formal, often governmental or religious, with legally enforced penalties.
- Focus shifted over time—from blasphemy/heresy toward indecency, sedition, political and ethnic slurs (pp. 13–15).
## Language Change and Innovation
- Taboo motivates invention: “Poetic inventiveness” and new vocabulary arise as taboos push speakers to “create highly inventive and often playful new expressions” (p. 2).
- Taboo prompts “word addition, word loss, sound change and semantic shift” (p. 2).
- New terms constantly “arise by a changed form for the tabooed expression and by figurative language sparked by perceptions ... of the denotata about faeces, menstrual blood, genitals, death and so on” (p. 2).
- Taboos “play havoc with the standard methods of historical linguistics by undermining the supposed arbitrary link between the meaning and form of words” (p. 2).
## Origins and Colonial Encounter: Word “Taboo”
- “The English word taboo derives from the Tongan tabu… [which] means simply ‘to forbid’, ‘forbidden’, and can be applied to any sort of prohibition,” from etiquette to “an order issued by a chief” (p. 2–3).
- Accounts of Tahitian food taboos and gendered prohibitions (“the women never upon any account eat with the men, but always by themselves”) (p. 3); legitimacy or social status marked through eating habits.
- Social function parallels US caste/race systems—exclusion practices reinforce social hierarchy (p. 4).
## Fatal, Demonic, and Dangerous Taboos
- Inherent danger: “A nineteenth-century view, attributable directly to Wundt’s folk psychology, is a belief... that there is a ‘demonic’ power within a tabooed object comparable with the dangerous power of a Polynesian chief or the Emperor of Japan or Satan himself” (p. 5).
- Psychosomatic effect: “Cases are on record in which persons who had unwittingly broken a taboo actually died of terror on discovering their fatal error, writes Frazer” (p. 5).
- Some anthropologists argue only certain extremely dangerous taboos (e.g., incest, sacrilege) are believed to carry supernatural consequences (p. 5).
## Rituals and Repair After Violation
- Violations often require purification—e.g., “the elders take a sheep and place it on the woman’s shoulders, and it is then killed ... [announcing] that they are severing the bond of blood relationship that exists between the pair. A medicine man then comes and purifies the couple” (Kikuyu, p. 6).
- Balinese build only one-story houses to avoid being under “unclean” garments; Christians confess sins; spitting used for expiation in southern Africa (p. 6).
- Ritual persists even as original motivations fade: many become “symbolic idiom” after the source is forgotten (p. 8).
## Taboos as Strategy: Exploitation and Symbolic Power
- Strategic tabooing keeps others away (e.g., land, trees, or objects marked as belonging to chiefs or spirits to deter theft) (p. 7).
- Gender and power: women’s genital organs viewed as “potent means of defeating evil”; exposing the vulva to demons drives evil away (pp. 7–8).
- Symbolic authority: names, body parts, and behaviors may be tabooed for status/power or to assert territorial/customary rights (p. 7).
## The Relativity and Contextuality of Taboo
- No universal taboo: “Nothing is taboo for all people, under all circumstances, for all time... Every taboo must be specified for a particular community of people, for a specified context, at a given place and time” (p. 10–11).
- Variation examples: “Women ... exposed one or both breasts in public ... as a display of youth and beauty” in 17th-century Europe; taboo today. Visual representations of nudity or death vary dramatically by culture (p. 10).
- Taboo applies to behavior, not objects: “Where something physical or metaphysical is said to be tabooed, what is in fact tabooed is its interaction with an individual... In short, a taboo applies to behaviour” (p. 11).
- Deliberate flouting: during conscious violation, “the so-called taboo is flouted... [it] does not function as a taboo for the perpetrator” (p. 10).
## 12. Psycholinguistic Contamination and “Strong Language”
- Core idea: taboo words are “contaminated” by their association with taboo referents; the denotatum taints the word itself.
- Quote: “Society’s perception of a dirty word’s tainted denotatum contaminates the word itself and we discuss how the saliency of obscenity and dysphemism makes the description ‘strong language’ particularly appropriate” (p. 1).
- Expansion: Burridge suggests a quasi-psychological mechanism; signifier inherits stigma of the signified, explaining euphemism cycles (“lavatory” → “toilet” → “bathroom” → “restroom”).
- Implication: Taboo operates semiotically—contamination spreads through linguistic signs, not just physical contact.
## 13. Mary Douglas’s “Matter Out of Place”
- Core idea: Douglas’s theory of pollution is structuralist—dirt is “matter out of place”; taboo marks category boundaries.
- Quote: “Mary Douglas’s anthropological study of ritual pollution offers insights here. As she saw it, the distinction between cleanliness and filth stems from the basic human need to structure experience and render it understandable. That which is taboo threatens chaos and disorder” (p. 8).
- Expansion: Taboo is not about intrinsic danger of substances (blood, corpses, menstruation) but about violations of categorical boundaries (life/death, male/female, pure/impure).
- Implication: Taboo is a cognitive and cultural ordering device enforcing symbolic boundaries.
## 14. Social Cohesion and Boundary-Work
- Core idea: shared taboos bind communities together.
- Quote: “Shared taboos are therefore a sign of social cohesion. Moreover, as part of a wider belief system, they provide the basis people need to function in an otherwise confused and hostile environment” (p. 8).
- Expansion: Taboo is constitutive—it creates “us” vs. “them,” reinforcing group identity.
- Implication: Taboo performs boundary-work, policing acceptable speech/behavior to define the community.
## 15. Language as Shield, Weapon, and Release
- Core idea: language is strategic in relation to taboo.
- Quote: “Language is used as a shield against malign fate and the disapprobation of fellow human beings; it is used as a weapon against enemies and as a release valve when we are angry, frustrated or hurt” (p. 2).
- Expansion: Euphemism shields, dysphemism weaponizes, swearing releases tension; taboo structures the emotional economy of language.
- Implication: Taboo enables expressive force, catharsis, and aggression, not merely avoidance.
## 16. Authority Behind Taboos
- Core idea: taboos are enforced by multiple sources of authority.
- Quote: “The constraint on behaviour is imposed by someone or some physical or metaphysical force that the individual believes has authority or power over them—the law, the gods, the society in which one lives, even proprioceptions” (pp. 7–8).
- Expansion: Authority is plural—legal, religious, social, bodily—undermining single explanatory models.
- Implication: Taboo is a multi-level regulatory system spanning external institutions and internalized bodily responses.
## 17. Political Correctness and Contemporary Censorship
- Core idea: modern tabooing behavior manifests as political correctness and linguistic prescription.
- Quote: “Political correctness and linguistic prescription are aspects of tabooing behaviour” (p. 1).
- Expansion: As older taboos policed sexuality or blasphemy, contemporary taboos police race, gender, and identity categories.
- Quote: “There is software to sanitize DVDs … filters sensitive to sex, drug use, some violence, profanity and crude language and bodily humor to skip scenes. It is nationalistic and politically biased” (p. 26).
- Implication: Taboo evolves with cultural priorities; mechanisms of censoring remain constant.
## 18. Taboo as Creative Force in Language
- Core idea: taboo drives euphemistic innovation and linguistic creativity.
- Quote: “Taboo and the consequent censoring of language motivate language change by promoting the creation of highly inventive and often playful new expressions… These creations occasionally rival Shakespeare” (p. 2).
- Expansion: Avoidance of taboo generates cycles of euphemism → contamination → replacement, acting as a motor of semantic change.
- Implication: Taboo is paradoxically productive. It restricts speech but enriches language.
## 🧩 Synthesis
- Taboo is not static: relative, context-dependent, historically shifting.
- Taboo is multi-functional: protects, polices, empowers, and creates.
- Taboo is systemic: operates across language, ritual, law, and social identity.
- Taboo is generative: produces linguistic creativity and cultural cohesion even as it restricts.

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# The Digital Garden
Once upon a time, in a world where code grew like vines, there lived a developer named Alex who discovered something magical in their repository.
Alex had been debugging a particularly stubborn bug for three days straight. The error messages were cryptic, the stack traces were confusing, and coffee had stopped working its usual magic.
## The Discovery
On the fourth morning, while scrolling through documentation that made less sense than the bug itself, Alex noticed something strange. Every time they ran their tests, a new file appeared in the `mysteries/` directory—a file that hadn't been there before.
The file was called `clue.md` and it contained only this:
> "Look not in the code, but between the lines."
## The Revelation
At first, Alex thought it was a prank from a coworker or a stray script. But as the days passed, the file began to change. New clues appeared, each more cryptic than the last.
It wasn't until Alex uploaded one of these files to their LLM Council that everything clicked. The council of AI models analyzed the patterns, the timing, the syntax. They saw connections Alex had missed.
## The Solution
The bug wasn't in Alex's code at all. It was in the dependencies, hidden in a nested module that updated itself every time the tests ran. The mysterious files were breadcrumbs left by a self-modifying dependency that was trying to communicate.
Alex fixed the issue, updated the dependencies, and the mysterious files stopped appearing. But they kept one—the first `clue.md`—as a reminder that sometimes the solution lies not in what you're looking for, but in what finds you.
---
*The end. Or is it just the beginning?*

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