From 93f9a130ea09bdc2ee3797d2a68adb4da92bf139 Mon Sep 17 00:00:00 2001 From: Aravind N Date: Thu, 14 May 2026 14:31:35 -0400 Subject: [PATCH] Add current codebase for retroactive review --- .github/ISSUE_TEMPLATE/bug_report.md | 49 + .github/ISSUE_TEMPLATE/config.yml | 8 + .github/ISSUE_TEMPLATE/feature_request.md | 52 + .github/dependabot.yml | 17 + .github/pull_request_template.md | 53 + .github/workflows/code_checks.yml | 67 + .github/workflows/docs.yml | 102 + .github/workflows/integration_tests.yml | 70 + .github/workflows/publish.yml | 50 + .github/workflows/unit_tests.yml | 70 + .gitignore | 86 + .pre-commit-config.yaml | 86 + .python-version | 1 + CODE_OF_CONDUCT.md | 128 + CONTRIBUTING.md | 26 + LICENSE.md | 201 + README.md | 309 + codecov.yml | 19 + configs/agent_config.yaml | 95 + docs/api.md | 142 + docs/architecture.md | 227 + docs/assets/favicon-48x48.svg | 9 + docs/assets/favicon.ico | Bin 0 -> 15086 bytes docs/assets/vector-logo.svg | 172 + docs/index.md | 130 + docs/overrides/partials/copyright.html | 22 + docs/overrides/partials/logo.html | 5 + docs/stylesheets/extra.css | 91 + docs/user_guide.md | 367 + mkdocs.yml | 84 + pyproject.toml | 114 + scripts/run_agent.py | 318 + scripts/run_agent_interactive.py | 475 ++ scripts/verify_setup.py | 222 + slurm/run_sonic_agent_native.sh | 64 + src/sonic_o1_agent/__init__.py | 7 + src/sonic_o1_agent/agents/__init__.py | 18 + src/sonic_o1_agent/agents/planner.py | 204 + src/sonic_o1_agent/agents/planner_advanced.py | 287 + src/sonic_o1_agent/agents/reasoner.py | 457 ++ src/sonic_o1_agent/agents/reflection.py | 360 + src/sonic_o1_agent/agents/sonic_agent.py | 212 + src/sonic_o1_agent/api.py | 385 + src/sonic_o1_agent/core/__init__.py | 24 + src/sonic_o1_agent/core/audio_processor.py | 198 + src/sonic_o1_agent/core/multimodal_engine.py | 245 + src/sonic_o1_agent/core/multimodal_utils.py | 116 + src/sonic_o1_agent/core/video_processor.py | 316 + .../Vector-Logo-FullColour_Horizontal.png | Bin 0 -> 18330 bytes src/sonic_o1_agent/demo/static/script.js | 723 ++ src/sonic_o1_agent/demo/static/style.css | 1010 +++ src/sonic_o1_agent/demo/templates/index.html | 250 + src/sonic_o1_agent/models/__init__.py | 8 + src/sonic_o1_agent/models/qwen_model.py | 962 +++ src/sonic_o1_agent/processors/__init__.py | 9 + .../processors/prompt_builder.py | 223 + .../processors/temporal_index.py | 312 + src/sonic_o1_agent/py.typed | 0 src/sonic_o1_agent/utils/__init__.py | 8 + src/sonic_o1_agent/utils/segmenter.py | 175 + src/sonic_o1_agent/workflows/__init__.py | 9 + src/sonic_o1_agent/workflows/graph.py | 146 + src/sonic_o1_agent/workflows/nodes.py | 539 ++ src/sonic_o1_agent/workflows/state.py | 65 + tests/__init__.py | 4 + tests/conftest.py | 30 + tests/integration/__init__.py | 4 + tests/integration/test_agent_workflow.py | 280 + tests/unit/test_multimodal_engine.py | 147 + tests/unit/test_planner.py | 115 + tests/unit/test_planner_advanced.py | 90 + tests/unit/test_prompt_builder.py | 133 + uv.lock | 7208 +++++++++++++++++ 73 files changed, 19210 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md create mode 100644 .github/ISSUE_TEMPLATE/config.yml create mode 100644 .github/ISSUE_TEMPLATE/feature_request.md create mode 100644 .github/dependabot.yml create mode 100644 .github/pull_request_template.md create mode 100644 .github/workflows/code_checks.yml create mode 100644 .github/workflows/docs.yml create mode 100644 .github/workflows/integration_tests.yml create mode 100644 .github/workflows/publish.yml create mode 100644 .github/workflows/unit_tests.yml create mode 100644 .gitignore create mode 100644 .pre-commit-config.yaml create mode 100644 .python-version create mode 100644 CODE_OF_CONDUCT.md create mode 100644 CONTRIBUTING.md create mode 100644 LICENSE.md create mode 100644 README.md create mode 100644 codecov.yml create mode 100644 configs/agent_config.yaml create mode 100644 docs/api.md create mode 100644 docs/architecture.md create mode 100644 docs/assets/favicon-48x48.svg create mode 100644 docs/assets/favicon.ico create mode 100644 docs/assets/vector-logo.svg create mode 100644 docs/index.md create mode 100644 docs/overrides/partials/copyright.html create mode 100644 docs/overrides/partials/logo.html create mode 100644 docs/stylesheets/extra.css create mode 100644 docs/user_guide.md create mode 100644 mkdocs.yml create mode 100644 pyproject.toml create mode 100644 scripts/run_agent.py create mode 100644 scripts/run_agent_interactive.py create mode 100644 scripts/verify_setup.py create mode 100644 slurm/run_sonic_agent_native.sh create mode 100644 src/sonic_o1_agent/__init__.py create mode 100644 src/sonic_o1_agent/agents/__init__.py create mode 100644 src/sonic_o1_agent/agents/planner.py create mode 100644 src/sonic_o1_agent/agents/planner_advanced.py create mode 100644 src/sonic_o1_agent/agents/reasoner.py create mode 100644 src/sonic_o1_agent/agents/reflection.py create mode 100644 src/sonic_o1_agent/agents/sonic_agent.py create mode 100644 src/sonic_o1_agent/api.py create mode 100644 src/sonic_o1_agent/core/__init__.py create mode 100644 src/sonic_o1_agent/core/audio_processor.py create mode 100644 src/sonic_o1_agent/core/multimodal_engine.py create mode 100644 src/sonic_o1_agent/core/multimodal_utils.py create mode 100644 src/sonic_o1_agent/core/video_processor.py create mode 100644 src/sonic_o1_agent/demo/static/Vector-Logo-FullColour_Horizontal.png create mode 100644 src/sonic_o1_agent/demo/static/script.js create mode 100644 src/sonic_o1_agent/demo/static/style.css create mode 100644 src/sonic_o1_agent/demo/templates/index.html create mode 100644 src/sonic_o1_agent/models/__init__.py create mode 100644 src/sonic_o1_agent/models/qwen_model.py create mode 100644 src/sonic_o1_agent/processors/__init__.py create mode 100644 src/sonic_o1_agent/processors/prompt_builder.py create mode 100644 src/sonic_o1_agent/processors/temporal_index.py create mode 100644 src/sonic_o1_agent/py.typed create mode 100644 src/sonic_o1_agent/utils/__init__.py create mode 100644 src/sonic_o1_agent/utils/segmenter.py create mode 100644 src/sonic_o1_agent/workflows/__init__.py create mode 100644 src/sonic_o1_agent/workflows/graph.py create mode 100644 src/sonic_o1_agent/workflows/nodes.py create mode 100644 src/sonic_o1_agent/workflows/state.py create mode 100644 tests/__init__.py create mode 100644 tests/conftest.py create mode 100644 tests/integration/__init__.py create mode 100644 tests/integration/test_agent_workflow.py create mode 100644 tests/unit/test_multimodal_engine.py create mode 100644 tests/unit/test_planner.py create mode 100644 tests/unit/test_planner_advanced.py create mode 100644 tests/unit/test_prompt_builder.py create mode 100644 uv.lock diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..f2854ab --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,49 @@ +--- +name: πŸ› Bug Report +about: Report a bug to help us improve the project +title: '[BUG] ' +labels: 'bug' +assignees: '' + +--- + +## Bug Description + + + +## Steps to Reproduce + +1. +2. +3. + +**Code snippet (if applicable):** +```python +# Your code here +``` + +## Expected Behavior + + + +## Actual Behavior + + + +## Environment + +- **Component:** [API | Core functionality | Any] +- **OS:** [Windows | MacOS | Linux] +- **Python Version:** + +## Screenshots/Logs + + + +## Additional Context + + + +## Possible Solution + + diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..9a27642 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,8 @@ +blank_issues_enabled: false +contact_links: + - name: πŸ’¬ Discussion + url: https://github.com/VectorInstitute/aieng-template-uv/discussions + about: Ask questions or discuss ideas with the community + - name: πŸ“– Documentation + url: https://github.com/VectorInstitute/aieng-template-uv#readme + about: Check the documentation for setup and usage guides diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..8ff6625 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,52 @@ +--- +name: ✨ Feature Request +about: Suggest a new feature or enhancement +title: '[FEATURE] ' +labels: 'enhancement' +assignees: '' + +--- + +## Problem Statement + + + +## Proposed Solution + + + +## Alternative Solutions + + + +## Use Cases + + +- +- +- + +## Implementation Ideas + + + +## Component Impact + + +- [ ] API +- [ ] Core functionality +- [ ] Docker/Infrastructure +- [ ] Documentation +- [ ] Any other part of the system + +## Additional Context + + + +## Priority + + +- [ ] Nice to have +- [ ] Would be helpful +- [ ] Important for my use case +- [ ] Critical/blocking diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..ea8c4ac --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,17 @@ +# To get started with Dependabot version updates, you'll need to specify which +# package ecosystems to update and where the package manifests are located. +# Please see the documentation for all configuration options: +# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file + +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" # Location of package manifests + schedule: + interval: "daily" + + # Keep uv dependencies (uv.lock) up to date + - package-ecosystem: "uv" + directory: "/" # where pyproject.toml and uv.lock live + schedule: + interval: "daily" diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000..8c195a3 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,53 @@ +## Summary + + + +Clickup Ticket(s): Link(s) if applicable. + +## Type of Change + +- [ ] πŸ› Bug fix (non-breaking change that fixes an issue) +- [ ] ✨ New feature (non-breaking change that adds functionality) +- [ ] πŸ’₯ Breaking change (fix or feature that would cause existing functionality to not work as expected) +- [ ] πŸ“ Documentation update +- [ ] πŸ”§ Refactoring (no functional changes) +- [ ] ⚑ Performance improvement +- [ ] πŸ§ͺ Test improvements +- [ ] πŸ”’ Security fix + +## Changes Made + + +- +- +- + +## Testing + + +- [ ] Tests pass locally (`uv run pytest tests/`) +- [ ] Type checking passes (`uv run mypy `) +- [ ] Linting passes (`uv run ruff check src_dir/`) +- [ ] Manual testing performed (describe below) + +**Manual testing details:** + + +## Screenshots/Recordings + + + +## Related Issues + + + +## Deployment Notes + + + +## Checklist + +- [ ] Code follows the project's style guidelines +- [ ] Self-review of code completed +- [ ] Documentation updated (if applicable) +- [ ] No sensitive information (API keys, credentials) exposed diff --git a/.github/workflows/code_checks.yml b/.github/workflows/code_checks.yml new file mode 100644 index 0000000..474760b --- /dev/null +++ b/.github/workflows/code_checks.yml @@ -0,0 +1,67 @@ +name: code checks +permissions: + contents: read + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - '**.py' + - uv.lock + - pyproject.toml + - '**.ipynb' + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - '**.py' + - uv.lock + - pyproject.toml + - '**.ipynb' + +jobs: + run-code-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6.0.2 + + - name: Install uv + uses: astral-sh/setup-uv@v7.3.0 + with: + # Install a specific version of uv. + version: "0.9.11" + enable-cache: true + + - name: "Set up Python" + uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Install dependencies and check code + run: | + source .venv/bin/activate + pre-commit run --all-files + + - name: Install pip in venv (for pip-audit) + run: uv pip install pip + + - name: pip-audit (gh-action-pip-audit) + uses: pypa/gh-action-pip-audit@v1.1.0 + with: + virtual-environment: .venv/ + ignore-vulns: | + GHSA-4xh5-x5gv-qwph + CVE-2025-69872 + CVE-2026-28277 + CVE-2026-32597 + CVE-2026-25960 + CVE-2026-25048 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..04cb45e --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,102 @@ +name: docs +permissions: + contents: write + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/docs.yml + - '**.py' + - '**.ipynb' + - '**.html' + - '**.js' + - '**.md' + - uv.lock + - pyproject.toml + - mkdocs.yml + - '**.png' + - '**.svg' + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/docs.yml + - '**.py' + - '**.ipynb' + - '**.js' + - '**.html' + - uv.lock + - pyproject.toml + - '**.md' + - mkdocs.yml + - '**.png' + - '**.svg' + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v6.0.2 + + - name: Install uv + uses: astral-sh/setup-uv@v7.3.0 + with: + version: "0.9.11" + enable-cache: true + + - name: Set up Python + uses: actions/setup-python@v6.2.0 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --extra docs + + - name: Build docs + run: uv run mkdocs build + env: + PYTHONPATH: src + + - name: Create .nojekyll file + run: touch site/.nojekyll + + - name: Upload artifact + uses: actions/upload-artifact@v6 + with: + name: docs-site + path: site/ + retention-days: 1 + + deploy: + needs: build + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v6.0.2 + + - name: Configure Git Credentials + run: | + git config user.name github-actions[bot] + git config user.email 41898282+github-actions[bot]@users.noreply.github.com + + - name: Download artifact + uses: actions/download-artifact@v7 + with: + name: docs-site + path: site + + - name: Ensure .nojekyll exists + run: touch site/.nojekyll + + - name: Deploy to Github pages + uses: JamesIves/github-pages-deploy-action@v4.8.0 + with: + branch: gh-pages + folder: site diff --git a/.github/workflows/integration_tests.yml b/.github/workflows/integration_tests.yml new file mode 100644 index 0000000..2b23919 --- /dev/null +++ b/.github/workflows/integration_tests.yml @@ -0,0 +1,70 @@ +name: integration tests +permissions: + contents: read + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - .github/workflows/docs.yml + - .github/workflows/unit_tests.yml + - .github/workflows/integration_tests.yml + - '**.py' + - '**.ipynb' + - uv.lock + - pyproject.toml + - '**.rst' + - '**.md' + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - .github/workflows/docs.yml + - .github/workflows/unit_tests.yml + - .github/workflows/integration_tests.yml + - '**.py' + - '**.ipynb' + - uv.lock + - pyproject.toml + - '**.rst' + - '**.md' + +jobs: + integration-tests: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6.0.2 + + - name: Install uv + uses: astral-sh/setup-uv@v7.3.0 + with: + # Install a specific version of uv. + version: "0.9.11" + enable-cache: true + + - name: "Set up Python" + uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Run integration tests + run: | + uv run pytest tests/integration --cov=src/sonic_o1_agent --cov-report=xml + + # Uncomment this once this repo is configured on Codecov + - name: Upload coverage to Codecov + uses: codecov/codecov-action@671740ac38dd9b0130fbe1cec585b89eea48d3de + with: + token: ${{ secrets.CODECOV_TOKEN }} + slug: VectorInstitute/sonic-o1-agent + fail_ci_if_error: false + verbose: true diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml new file mode 100644 index 0000000..10fbbdd --- /dev/null +++ b/.github/workflows/publish.yml @@ -0,0 +1,50 @@ +name: publish package +permissions: + contents: read + pull-requests: write + +on: + push: + tags: + - "v*" + +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - name: Install apt dependencies + run: | + sudo apt-get update + sudo apt-get install libcurl4-openssl-dev libssl-dev + - uses: actions/checkout@v6.0.2 + + - name: Install uv + uses: astral-sh/setup-uv@v7.3.0 + with: + # Install a specific version of uv. + version: "0.9.11" + enable-cache: true + + - name: "Set up Python" + uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Build package + run: uv build + + - name: Publish package + uses: pypa/gh-action-pypi-publish@ed0c53931b1dc9bd32cbe73a98c7f6766f8a527e + with: + user: __token__ + password: ${{ secrets.PYPI_API_TOKEN }} + + - name: Create GitHub Release + id: create_release + uses: ncipollo/release-action@b7eabc95ff50cbeeedec83973935c8f306dfcd0b # v1.20.0 + with: + artifacts: "dist/*" + generateReleaseNotes: true diff --git a/.github/workflows/unit_tests.yml b/.github/workflows/unit_tests.yml new file mode 100644 index 0000000..784e8a2 --- /dev/null +++ b/.github/workflows/unit_tests.yml @@ -0,0 +1,70 @@ +name: unit tests +permissions: + contents: read + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - .github/workflows/docs.yml + - .github/workflows/unit_tests.yml + - .github/workflows/integration_tests.yml + - '**.py' + - '**.ipynb' + - uv.lock + - pyproject.toml + - '**.rst' + - '**.md' + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - .github/workflows/docs.yml + - .github/workflows/unit_tests.yml + - .github/workflows/integration_tests.yml + - '**.py' + - '**.ipynb' + - uv.lock + - pyproject.toml + - '**.rst' + - '**.md' + +jobs: + unit-tests: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v6.0.2 + + - name: Install uv + uses: astral-sh/setup-uv@v7.3.0 + with: + # Install a specific version of uv. + version: "0.9.11" + enable-cache: true + + - name: "Set up Python" + uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Run unit tests + run: | + uv run pytest tests/unit --cov=src/sonic_o1_agent --cov-report=xml + + # Uncomment this once this repo is configured on Codecov + - name: Upload coverage to Codecov + uses: codecov/codecov-action@671740ac38dd9b0130fbe1cec585b89eea48d3de + with: + token: ${{ secrets.CODECOV_TOKEN }} + slug: VectorInstitute/sonic-o1-agent + fail_ci_if_error: false + verbose: true diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..dd05000 --- /dev/null +++ b/.gitignore @@ -0,0 +1,86 @@ +# Python-generated files +__pycache__/ +*.py[oc] +build/ +dist/ +wheels/ +*.egg-info + +# Virtual environments +.venv +.venv_*/ +env/ +venv/ + +# Lint & Test +.mypy_cache/ +.pytest_cache/ +.ruff_cache/ +.coverage +htmlcov/ +*.cover + +# IDE +.vscode/ +.idea/ +*.swp +*.swo +*~ + +# macOS +*.DS_Store +.AppleDouble +.LSOverride + +# Documentation +site/ +docs/_build/ + +# Logs +logs/ +*.log +*.out + +# Temporary files +tmp/ +temp/ +*.tmp +.cache/ + +# Data directories (user-specific files) +data/raw/* +data/processed/* +data/outputs/* +!data/raw/.gitkeep +!data/processed/.gitkeep +!data/outputs/.gitkeep + +# Local configuration and scripts +slurm/verify_in_container.sh +slurm/run_sonic_agent.sh +slurm/run_interactive.sh +slurm/run_precommit.sh + + +# Model checkpoints and weights +*.pt +*.pth +*.ckpt +*.safetensors + +# Environment variables +.env +.env.local + +# Archive folder +.archive/ + +# Backup files +*.backup +*.bak +*.orig + + +coverage.xml + +.cursor/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..d860a20 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,86 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v6.0.0 # Use the ref you want to point at + hooks: + - id: trailing-whitespace + - id: check-ast + - id: check-builtin-literals + - id: check-docstring-first + - id: check-executables-have-shebangs + - id: debug-statements + - id: end-of-file-fixer + - id: mixed-line-ending + args: [--fix=lf] + - id: fix-byte-order-marker + - id: check-merge-conflict + - id: check-symlinks + - id: detect-private-key + - id: check-yaml + args: [--unsafe] + - id: check-toml + + - repo: https://github.com/astral-sh/uv-pre-commit + rev: 0.10.0 + hooks: + - id: uv-lock + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: 'v0.15.0' + hooks: + - id: ruff-check + args: [--fix, --exit-non-zero-on-fix] + types_or: [python, jupyter] + - id: ruff-format + types_or: [python, jupyter] + + - repo: https://github.com/pre-commit/mirrors-mypy + rev: v1.19.1 + hooks: + - id: mypy + args: [--config-file, pyproject.toml] + additional_dependencies: [types-PyYAML>=6.0.0] + types: [python] + exclude: "tests|src/sonic_o1_agent/api\\.py" + + - repo: https://github.com/crate-ci/typos + rev: v1.42.1 + hooks: + - id: typos + args: [] + + - repo: https://github.com/nbQA-dev/nbQA + rev: 1.9.1 + hooks: + - id: nbqa-ruff + args: [--fix, --exit-non-zero-on-fix] + + - repo: local + hooks: + - id: doctest + name: doctest + entry: python3 -m doctest -o NORMALIZE_WHITESPACE + files: "^src/sonic_o1_agent/" + exclude: "src/sonic_o1_agent/(demo|api\\.py)" + types: [python] + language: system + + - repo: local + hooks: + - id: pytest + name: pytest + entry: bash -c 'uv run pytest tests/unit' + language: system + pass_filenames: false + always_run: true + +ci: + autofix_commit_msg: | + [pre-commit.ci] Add auto fixes from pre-commit.com hooks + + for more information, see https://pre-commit.ci + autofix_prs: true + autoupdate_branch: '' + autoupdate_commit_msg: '[pre-commit.ci] pre-commit autoupdate' + autoupdate_schedule: weekly + skip: [pytest,doctest,mypy] + submodules: false diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..11478c8 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 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Rather than a single model call, it orchestrates a pipeline of specialized agents β€” each responsible for a distinct reasoning role β€” coordinated through a LangGraph workflow. + +**Agents:** + +- **Planner** β€” parses temporal references, detects relevant modalities, decides whether to segment the video to a sub-window, and optionally decomposes complex queries into ordered sub-tasks +- **Reasoner** β€” runs chain-of-thought analysis with explicit reasoning steps and self-verification +- **Reflection** β€” evaluates response quality, confidence-scores the output, and refines when recommended; supports iterative refinement and hallucination detection +- **Multimodal Engine** β€” memory-efficient video/audio processing via PyAV; builds a frame-captioned temporal index for longer videos to give the model accurate timestamp grounding + +**Stack:** Qwen3-Omni (native video + audio), vLLM (tensor-parallel inference), LangGraph (workflow orchestration), PyAV (media processing). + +``` +sonic-o1-agent/ +β”œβ”€β”€ src/sonic_o1_agent/ +β”‚ β”œβ”€β”€ agents/ +β”‚ β”‚ β”œβ”€β”€ sonic_agent.py # Main orchestrator +β”‚ β”‚ β”œβ”€β”€ planner.py # Planning agent +β”‚ β”‚ β”œβ”€β”€ planner_advanced.py # Multi-step decomposition +β”‚ β”‚ β”œβ”€β”€ reasoner.py # Chain-of-Thought agent +β”‚ β”‚ └── reflection.py # Self-reflection agent +β”‚ β”œβ”€β”€ models/ +β”‚ β”‚ └── qwen_model.py # Qwen3-Omni + vLLM wrapper (embedded & server mode) +β”‚ β”œβ”€β”€ core/ +β”‚ β”‚ β”œβ”€β”€ multimodal_engine.py # Orchestration +β”‚ β”‚ β”œβ”€β”€ video_processor.py # PyAV video processing +β”‚ β”‚ β”œβ”€β”€ audio_processor.py # PyAV audio processing +β”‚ β”‚ └── multimodal_utils.py # Shared constants & math helpers +β”‚ β”œβ”€β”€ processors/ +β”‚ β”‚ β”œβ”€β”€ prompt_builder.py # Dynamic prompt construction +β”‚ β”‚ └── temporal_index.py # Frame-captioned segment grounding +β”‚ β”œβ”€β”€ workflows/ +β”‚ β”‚ β”œβ”€β”€ state.py # SonicState schema +β”‚ β”‚ β”œβ”€β”€ nodes.py # LangGraph node functions +β”‚ β”‚ └── graph.py # StateGraph with conditional edges +β”‚ β”œβ”€β”€ api.py # FastAPI demo server +β”‚ β”œβ”€β”€ demo/ # Demo UI (HTML/CSS/JS) +β”‚ └── utils/ +β”‚ └── segmenter.py # ffmpeg-based segmentation +β”œβ”€β”€ configs/ +β”‚ └── agent_config.yaml +β”œβ”€β”€ scripts/ +β”‚ β”œβ”€β”€ run_agent.py +β”‚ └── verify_setup.py +β”œβ”€β”€ slurm/ +β”‚ └── run_sonic_agent_native.sh +└── tests/ +``` + +--- + +## Demo UI + +> **[β–Ά Watch the demo video](https://drive.google.com/file/d/1e1qv4JCKqeDc7UdZTIx1-c39zqXNKWtr/view?usp=sharing)** + +The demo provides a web interface for uploading videos and querying the multi-agent pipeline. It streams real-time progress as each agent completes β€” planning, temporal indexing, inference, and reflection β€” with a live thinking panel showing intermediate results. + +### Running the Demo (vLLM Server Mode) + +The demo connects to a running vLLM server via the OpenAI-compatible API. This separates model serving from the application, enabling continuous batching and concurrent requests (e.g., parallel temporal indexing). + +**1. Start the vLLM server:** + +```bash +vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct \ + --port 8080 --host 0.0.0.0 \ + --dtype bfloat16 --max-model-len 32768 \ + --allowed-local-media-path / \ + -tp 1 +``` + +Adjust `-tp` to match your GPU count (e.g., `-tp 4` for 4 GPUs, `--max-model-len 65536`). + +**2. Start the demo server** (in a separate terminal on the same node): + +```bash +cd sonic-o1-agent +PYTHONPATH=src python -c " +from sonic_o1_agent.api import serve +serve(vllm_base_url='http://localhost:8080/v1', port=8000, config_path='configs/agent_config.yaml') +" +``` + +**3. Open in your browser:** + +``` +http://localhost:8000 +``` + +If running on a remote server, forward the port: + +```bash +ssh -L 8000::8000 user@server +``` + +### Server Mode Configuration + +To use server mode from the CLI (without the demo UI), add `vllm_base_url` to your config: + +```yaml +model: + vllm_base_url: "http://localhost:8080/v1" # set to "" or remove for embedded mode + model_path: "Qwen/Qwen3-Omni-30B-A3B-Instruct" +``` + +Or set it via environment variable: + +```bash +VLLM_BASE_URL=http://localhost:8080/v1 python scripts/run_agent.py \ + --config configs/agent_config.yaml \ + --video video.mp4 --audio audio.m4a \ + --query "Summarize the key points" --all-features +``` + +When `vllm_base_url` is empty or absent, the system falls back to embedded mode (loads the model in-process) β€” all existing usage is unchanged. + +--- + +## How It Works + +``` +User Query + ↓ +Planning β€” parse temporal refs, detect modality, optionally decompose into sub-tasks + ↓ +Segmentation β€” extract time window if query references a specific range + ↓ +Temporal Index β€” for longer videos: caption N segments β†’ inject timestamped index into prompt + ↓ +Inference β€” multi_step | reasoning | direct (selected based on flags) + ↓ +Reflection β€” evaluate quality, refine if needed, optional hallucination check + ↓ +Cleanup β†’ Final Answer +``` + +**Agent modes:** + +| Mode | Use when | Flag | +|---|---|---| +| Direct | Simple queries | *(default)* | +| Reasoning | Step-by-step analysis needed | `--reasoning` | +| Reflective | Quality-critical output | `--reflection` | +| Multi-Step | Comparisons, causal, multi-entity | `--multi-step` | +| Full | Maximum capability | `--all-features` | + +--- + +## Configuration + +`configs/agent_config.yaml`: + +```yaml +model: + model_path: "Qwen/Qwen3-Omni-30B-A3B-Instruct" + use_thinking: false + # vLLM backend mode (optional): + # - Set URL to use server mode (connects to running vllm serve) + # - Empty or omitted = embedded mode (loads model in-process) + # vllm_base_url: "http://localhost:8080/v1" + + # vLLM settings + gpu_memory_utilization: 0.85 + tensor_parallel_size: 4 # Number of GPUs + max_num_seqs: 1 + max_model_len: 65536 + + # Generation settings + generation_config: + temperature: 0.0 + top_p: 0.95 + top_k: 20 + max_new_tokens: 8192 + + # Multimodal limits + limit_mm_per_prompt: + image: 1 + video: 1 + audio: 1 + +processing: + # Default processing parameters + max_frames: 64 + max_audio_chunks: 32 # null = no chunking, or specify int + audio_chunk_duration_sec: 10.0 + + # Segmentation thresholds + min_video_duration_for_segmentation: 300 # 5 minutes + segment_efficiency_threshold: 0.8 # Segment if <80% of total + +# Chain-of-Thought Reasoning Settings +reasoning: + max_reasoning_steps: 5 # Maximum CoT steps + enable_verification: true # Enable verification step + +# Self-Reflection Settings +reflection: + confidence_threshold: 0.7 # Minimum confidence to accept + max_refinement_attempts: 2 # Max iterations for refinement + use_iterative_refinement: true # Set true to loop until confidence threshold + check_hallucination: true # Check for hallucination + +# Temporal Index Settings (frame-captioned segment grounding) +temporal_index: + min_duration_sec: 180 # Skip indexing for videos shorter than this + num_segments: 10 # Number of segments to split the video into + max_frames_per_segment: 16 # Frames sampled per segment caption + caption_max_tokens: 128 # Max tokens per segment caption + +# Multi-Step Planning Settings +planning: + max_steps: 10 # Maximum decomposition steps + enable_auto_decompose: true # Auto-detect complex queries +``` + +--- + +## Research Applications + +Legal analysis, medical review, education, compliance monitoring, qualitative research. + +--- + +## Quick Start + +```bash +# 1. Set your model path +vim configs/agent_config.yaml + +# 2. Edit paths in the SLURM script +vim slurm/run_sonic_agent_native.sh + +# 3. Submit +sbatch slurm/run_sonic_agent_native.sh + +# 4. Monitor +tail -f logs/sonic_agent_*.out +``` + +--- + +## Usage + +```bash +# Basic inference +python scripts/run_agent.py \ + --video hearing.mp4 --audio hearing.m4a \ + --query "Summarize the key arguments" + +# Chain-of-thought reasoning +python scripts/run_agent.py --video hearing.mp4 \ + --query "Analyze the legal strategy" --reasoning + +# Self-reflection +python scripts/run_agent.py --video hearing.mp4 \ + --query "What inconsistencies exist?" --reflection + +# Multi-step decomposition +python scripts/run_agent.py --video trial.mp4 \ + --query "Compare defense vs prosecution arguments" --multi-step + +# All agents + save output +python scripts/run_agent.py --video complex.mp4 \ + --query "Comprehensive analysis with contradictions" \ + --all-features --verbose --output results.json + +# Temporal query (auto-segments the relevant window) +python scripts/run_agent.py --video hearing.mp4 \ + --query "What happened between minute 5 and 10?" +``` + +--- + +## Citation + +```bibtex +@software{sonic_o1_multi_agent, + author = {Radwan, Ahmed Y.}, + title = {Sonic O1: A Multi-Agent System for Audio-Video Understanding}, + year = {2026}, + publisher = {Vector Institute}, +} +``` + +--- + +## Contact + +**Shaina Raza** - shaina.raza@vectorinstitute.ai + + +Vector Institute for Artificial Intelligence + +--- + +## Acknowledgments + +Resources provided in part by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute. + +Funded by the EU Horizon Europe programme β€” AIXPERT project (Grant No. 101214389). + +Built on [Qwen3-Omni](https://github.com/QwenLM/Qwen3-Omni), [vLLM](https://github.com/vllm-project/vllm), and the [Vector Institute AI Engineering Template](https://github.com/VectorInstitute/aieng-template-uv). diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 0000000..72f5c36 --- /dev/null +++ b/codecov.yml @@ -0,0 +1,19 @@ +codecov: + require_ci_to_pass: true + notify: + after_n_builds: 2 + wait_for_ci: yes +comment: + behavior: default + layout: reach,diff,flags,tree,reach + show_carryforward_flags: false + require_changes: true +coverage: + status: + changes: true + default_rules: + flag_coverage_not_uploaded_behavior: include + patch: true + project: true +github_checks: + annotations: true diff --git a/configs/agent_config.yaml b/configs/agent_config.yaml new file mode 100644 index 0000000..81b882b --- /dev/null +++ b/configs/agent_config.yaml @@ -0,0 +1,95 @@ +# Sonic O1 Agent Configuration +# Author: Ahmed Y. Radwan, SONIC-O1 Team +# +# TEST CONFIG β€” Server mode enabled (connects to external vLLM serve) +# +# Start vLLM server first: +# vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct \ +# --port 8080 --host 0.0.0.0 \ +# --dtype bfloat16 --max-model-len 65536 \ +# --allowed-local-media-path / \ +# -tp 4 +# +# Then run agent: +# python scripts/run_agent.py \ +# --config configs/agent_config_server.yaml \ +# --video /path/to/video.mp4 \ +# --audio /path/to/audio.m4a \ +# --query "Summarize the key points" \ +# --all-features --verbose +# +# Or run demo: +# sonic-o1-agent serve \ +# --config configs/agent_config_server.yaml \ +# --port 8000 + +agent: + name: "sonic-o1-agent" + version: "0.1.0" + +model: + model_path: "Qwen/Qwen3-Omni-30B-A3B-Instruct" + use_thinking: false + + # ═══════════════════════════════════════════════════════════════════ + # SERVER MODE β€” set this to your running vLLM server URL + # ═══════════════════════════════════════════════════════════════════ + # Replace with your actual vLLM server address. + # Format: http://:/v1 + # + # If empty or omitted β†’ falls back to embedded mode (loads model + # in-process via vllm.LLM, same as original behaviour). + # + # Can also be overridden by VLLM_BASE_URL environment variable. + vllm_base_url: "http://localhost:8080/v1" # "" to use no server + + # ═══════════════════════════════════════════════════════════════════ + # Embedded vLLM settings (only used when vllm_base_url is empty) + # Kept here so you can switch back by clearing vllm_base_url + # ═══════════════════════════════════════════════════════════════════ + gpu_memory_utilization: 0.85 + tensor_parallel_size: 4 + max_num_seqs: 1 + max_model_len: 65536 + + # Generation settings (used in BOTH modes) + generation_config: + temperature: 0.0 + top_p: 0.95 + top_k: 20 + max_new_tokens: 8192 + + # Multimodal limits (embedded mode only) + limit_mm_per_prompt: + image: 1 + video: 1 + audio: 1 + +processing: + max_frames: 64 + max_audio_chunks: 32 + audio_chunk_duration_sec: 10.0 + min_video_duration_for_segmentation: 300 + segment_efficiency_threshold: 0.8 + + +reasoning: + max_reasoning_steps: 5 + enable_verification: true + +reflection: + confidence_threshold: 0.7 + max_refinement_attempts: 2 + use_iterative_refinement: true + check_hallucination: true + +temporal_index: + min_duration_sec: 180 + num_segments: 10 + max_frames_per_segment: 16 + caption_max_tokens: 128 + max_parallel: 10 + +planning: + max_steps: 10 + enable_auto_decompose: true diff --git a/docs/api.md b/docs/api.md new file mode 100644 index 0000000..a6aac19 --- /dev/null +++ b/docs/api.md @@ -0,0 +1,142 @@ +# API Reference + +## Top Level + +::: sonic_o1_agent + options: + show_root_heading: true + show_root_full_path: true + +--- + +## Agents + +### SonicAgent (Orchestrator) + +::: sonic_o1_agent.agents.sonic_agent + options: + show_root_heading: true + show_root_full_path: true + +### AgentPlanner + +::: sonic_o1_agent.agents.planner + options: + show_root_heading: true + show_root_full_path: true + +### ChainOfThoughtReasoner + +::: sonic_o1_agent.agents.reasoner + options: + show_root_heading: true + show_root_full_path: true + +### SelfReflection + +::: sonic_o1_agent.agents.reflection + options: + show_root_heading: true + show_root_full_path: true + +### MultiStepPlanner + +::: sonic_o1_agent.agents.planner_advanced + options: + show_root_heading: true + show_root_full_path: true + +--- + +## Models + +### Qwen3OmniModel + +::: sonic_o1_agent.models.qwen_model + options: + show_root_heading: true + show_root_full_path: true + +--- + +## API Server + +### Demo Server + +::: sonic_o1_agent.api + options: + show_root_heading: true + show_root_full_path: true + +--- + +## Processors + +### PromptBuilder + +::: sonic_o1_agent.processors.prompt_builder + options: + show_root_heading: true + show_root_full_path: true + +### TemporalIndexBuilder + +::: sonic_o1_agent.processors.temporal_index + options: + show_root_heading: true + show_root_full_path: true + +--- + +## Core + +### Multimodal Engine + +::: sonic_o1_agent.core.multimodal_engine + options: + show_root_heading: true + show_root_full_path: true + +**Note:** The multimodal engine has been refactored into focused modules: +- `multimodal_engine.py` - Orchestration functions (maintains backward compatibility) +- `video_processor.py` - Video processing functions +- `audio_processor.py` - Audio processing functions +- `multimodal_utils.py` - Shared utilities and constants + +All functions remain accessible through `multimodal_engine` for backward compatibility. + +--- + +## Workflows + +### Graph + +::: sonic_o1_agent.workflows.graph + options: + show_root_heading: true + show_root_full_path: true + +### Nodes + +::: sonic_o1_agent.workflows.nodes + options: + show_root_heading: true + show_root_full_path: true + +### State + +::: sonic_o1_agent.workflows.state + options: + show_root_heading: true + show_root_full_path: true + +--- + +## Utilities + +### VideoSegmenter + +::: sonic_o1_agent.utils.segmenter + options: + show_root_heading: true + show_root_full_path: true diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 0000000..5c342eb --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,227 @@ +# Architecture + +## Overview + +SONIC-O1 is a **compound multi-agent system** built on +[LangGraph](https://github.com/langchain-ai/langgraph). Each component is a +specialised node in a state-machine workflow. The graph executes sequentially +with conditional branching based on user-selected agent modes. + +--- + +## Multi-Agent Roles + +| Agent | Module | Responsibility | +|-------|--------|----------------| +| **Planner** | `agents/planner.py` | Parse temporal references, detect query type, determine segmentation, compute video duration | +| **Temporal Index Builder** | `processors/temporal_index.py` | Split video into segments, caption each via the VLM, assemble a timestamped text index | +| **Reasoner** | `agents/reasoner.py` | Chain-of-Thought: understand query, plan approach, execute analysis, verify, refine | +| **Reflection** | `agents/reflection.py` | Evaluate response quality, score confidence, iteratively refine if below threshold | +| **Prompt Builder** | `processors/prompt_builder.py` | Construct query-type-aware prompts with temporal grounding directives | +| **Multimodal Engine** | `core/multimodal_engine.py` | Orchestration functions (backward compatible) | +| **Video Processor** | `core/video_processor.py` | Video frame sampling (PyAV), metadata extraction | +| **Audio Processor** | `core/audio_processor.py` | Audio loading with time-range slicing, chunking | +| **Multimodal Utils** | `core/multimodal_utils.py` | Shared utilities, constants, math helpers | + +--- + +## Workflow Graph + +```text + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Planning β”‚ + β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β” + β”‚ Segmentation β”‚ + β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ Temporal Indexingβ”‚ + β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β” + β”‚ route: multi_step / β”‚ + β”‚ reasoning / direct β”‚ + β””β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”˜ + β”‚ β”‚ β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β–Όβ”€β”€β” β”Œβ”€β”€β”€β”€β”€β–Όβ”€β”€β”€β”€β” β”Œβ”€β”€β–Όβ”€β”€β”€β”€β”€β”€β” + β”‚MultiStepβ”‚ β”‚ Reasoningβ”‚ β”‚ Direct β”‚ + β””β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”˜ β””β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”˜ β””β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”˜ + β”‚ β”‚ β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β”΄β”€β”€β”€β”€β”€β”€β”€β” + β”‚use_reflection?β”‚ + β””β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”˜ + β”‚ β”‚ + β”Œβ”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β” β”Œβ”€β–Όβ”€β”€β”€β”€β”€β”€β” + β”‚Reflectionβ”‚ β”‚ Cleanupβ”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”˜ β””β”€β”€β”€β”¬β”€β”€β”€β”€β”˜ + β”‚ β”‚ + β”Œβ”€β”€β”€β–Όβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β–Όβ”€β”€β”€β” + β”‚ Cleanup β”‚ + β””β”€β”€β”€β”€β”€β”€β”€β”€β”¬β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜ + β”‚ + END +``` + +### State + +All nodes read from and write to a shared `SonicState` TypedDict that flows +through the graph. Key fields include: + +| Field | Type | Set By | +|-------|------|--------| +| `query` | `str` | User input | +| `plan` | `dict` | Planning node | +| `actual_video_path` | `str` | Segmentation node | +| `temporal_index` | `str` | Temporal indexing node | +| `response` | `str` | Reasoning or Direct node | +| `evidence` | `dict` | Direct node | +| `reflection` | `dict` | Reflection node | + +--- + +## Temporal Grounding + +Temporal grounding ensures the model cites **accurate timestamps in seconds** +rather than vague references. The strategy depends on video length. + +### Short Videos (< `min_duration_sec`) + +For videos below the configured threshold (default 180 s), the temporal +indexing step is skipped. The prompt builder injects a **sampling-context +hint** telling the model the video duration, frame count, and sampling +interval so it can estimate timestamps from visual progression. + +### Long Videos (>= `min_duration_sec`) + +A **frame-captioning index** is built before inference: + +1. **Segment computation** -- The video is divided into N equal-length + segments (capped by `num_segments`, minimum 30 s per segment). + +2. **Per-segment captioning** -- For each segment, the VLM receives only + the time-sliced video frames (`video_start` / `video_end`) **and** the + matching audio chunk (`audio_start` / `audio_end`). This avoids loading + the full file for every segment. + +3. **Index assembly** -- Captions are combined into a plain-text index: + + ```text + [0s - 92s] Speaker introduces the case and outlines the charges. + [92s - 184s] Defense attorney presents opening statement. + [184s - 277s] Prosecution shows exhibit A, a security recording. + ... + ``` + +4. **Prompt injection** -- The index is placed at the top of the main + inference prompt, before the user query, with a directive: + + > *IMPORTANT: A timestamped content index of the video is provided + > below. Use it to cite accurate timestamps in seconds when describing + > events.* + +This approach is inspired by the retrieve-then-read paradigm: expensive +multimodal perception is performed once per segment (cheap, parallel-ready), +and the downstream reasoning step operates on text -- which LLMs handle +with high fidelity. + +### Audio Slicing + +Both video and audio support time-range parameters: + +- **Video**: `video_start` / `video_end` restrict frame decoding via + `container.seek()` in PyAV. +- **Audio**: `audio_start` / `audio_end` map to the existing `offset` / + `duration` parameters in `load_audio_pyav`, loading only the relevant + samples. + +When no time range is specified (the main inference pass), the full file +is loaded as before. + +--- + +## Directory Structure + +```text +sonic-o1-agent/ +β”œβ”€β”€ src/sonic_o1_agent/ +β”‚ β”œβ”€β”€ api.py # FastAPI demo server (vLLM server mode) +β”‚ β”œβ”€β”€ demo/ # Demo UI (HTML/CSS/JS) +β”‚ β”œβ”€β”€ agents/ # Multi-Agent System +β”‚ β”‚ β”œβ”€β”€ sonic_agent.py # Main orchestrator +β”‚ β”‚ β”œβ”€β”€ planner.py # Planning agent +β”‚ β”‚ β”œβ”€β”€ planner_advanced.py # Multi-step decomposition +β”‚ β”‚ β”œβ”€β”€ reasoner.py # Chain-of-Thought agent +β”‚ β”‚ └── reflection.py # Self-reflection agent +β”‚ β”œβ”€β”€ models/ # Model wrappers +β”‚ β”‚ └── qwen_model.py # Qwen3-Omni + vLLM +β”‚ β”œβ”€β”€ core/ # Multimodal processing +β”‚ β”‚ β”œβ”€β”€ multimodal_engine.py # Orchestration (backward compatible) +β”‚ β”‚ β”œβ”€β”€ video_processor.py # Video processing (PyAV) +β”‚ β”‚ β”œβ”€β”€ audio_processor.py # Audio processing (PyAV) +β”‚ β”‚ └── multimodal_utils.py # Shared utilities & constants +β”‚ β”œβ”€β”€ processors/ # Prompt engineering & indexing +β”‚ β”‚ β”œβ”€β”€ prompt_builder.py # Dynamic prompt construction +β”‚ β”‚ └── temporal_index.py # Frame-captioned segment grounding +β”‚ β”œβ”€β”€ workflows/ # LangGraph orchestration +β”‚ β”‚ β”œβ”€β”€ state.py # SonicState (workflow state schema) +β”‚ β”‚ β”œβ”€β”€ nodes.py # All workflow node functions +β”‚ β”‚ └── graph.py # StateGraph with conditional edges +β”‚ └── utils/ # Utilities +β”‚ └── segmenter.py # Video/audio segmentation (ffmpeg) +β”œβ”€β”€ configs/ +β”‚ └── agent_config.yaml # System configuration +β”œβ”€β”€ scripts/ +β”‚ β”œβ”€β”€ run_agent.py # CLI entry point +β”‚ └── verify_setup.py # Setup verification +β”œβ”€β”€ slurm/ +β”‚ └── run_sonic_agent_native.sh # SLURM job script +β”œβ”€β”€ docs/ # MkDocs documentation +└── tests/ # Unit & integration tests +``` + +--- + +## Model Backend + +SONIC-O1 uses **Qwen3-Omni** served through **vLLM** for efficient +multi-GPU inference. + +| Feature | Detail | +|---------|--------| +| Model | Qwen3-Omni-30B-A3B-Instruct | +| Serving | vLLM with tensor parallelism | +| Context | Up to 65 536 tokens | +| Modalities | Native video + audio (no transcription needed) | +| Decoding | Greedy (`temperature=0.0`) for deterministic output | +| Caching | Prefix caching enabled for follow-up efficiency | + +The model wrapper (`qwen_model.py`) handles: + +- Lazy loading and engine recovery after crashes or OOM +- Chat template formatting via `Qwen3OmniMoeProcessor` +- Text-only generation for intermediate reasoning steps +- Multimodal generation with time-range-aware video and audio slicing + +### Dual Backend: Embedded vs Server Mode + +The model wrapper (`qwen_model.py`) supports two backends, selected by a +single config key: + +| Backend | When | How | +|---------|------|-----| +| **Embedded** | `vllm_base_url` absent or empty | Loads model in-process via `vllm.LLM`. Original behaviour. | +| **Server** | `vllm_base_url` set (e.g. `http://localhost:8080/v1`) | Connects to a running `vllm serve` via OpenAI SDK. | + +In server mode, video frames are **extracted client-side** using the same +`process_video_with_metadata` logic as embedded mode (respecting +`max_frames`, `min_frames`, and time-range slicing), then sent as base64 +JPEGs. This gives identical frame sampling behaviour regardless of backend +while enabling vLLM's continuous batching for concurrent requests. + +The temporal index builder automatically parallelises segment captioning +in server mode via `ThreadPoolExecutor`, sending multiple caption requests +simultaneously to the vLLM server. diff --git a/docs/assets/favicon-48x48.svg b/docs/assets/favicon-48x48.svg new file mode 100644 index 0000000..3cd92e5 --- /dev/null +++ b/docs/assets/favicon-48x48.svg @@ -0,0 +1,9 @@ + + + + + + + + + diff --git a/docs/assets/favicon.ico b/docs/assets/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..307623701502e65f152ae14dd97d014207cec956 GIT binary patch literal 15086 zcmeI2TWDNG7{@1VlHD4$@xeQ^DO$X1!A7yD8)>zO;tek^O1+@<_M$>xL^cmv%|=@F zO&`>PLZP6bwpLKvng=h4f~W`u(-JYd8{3qkZs<1S?|;rrda_MIw@G$$a6b6uo0&6b zzW;nVyE|vjF{a7PH_Mh8e23Y*)R-%bF&!PQ-fqmVw6)`P{LyA(*3;p9`f!T5AJiT! zWFTZ9WFTZ9WFTZ9RT=mNwXec%mS3w`V4=o^D7rW^1d5Q8WS3U{iRI3%Z_`zy|mku7zsz`jzADumxCUnonRQ z$W8{G-wlgF))~8L|J}+68uI~I3-7^GpzBxHK@WTiy3XE)YOB_-IbR8y|MPGSTm-+t zdeCR#I(Qa>g$$%U1NzMBlm2c>Ct_2oTe9^#B>m^8l0uVqaX5wA7dh>I4r!ahNJ9UP zw24n>SN^pINt1OW)?YW3GzoWFM-%#=PN9CEd-=4P){$QSP__8pOi63lxolp$yzZqE zW}6Dx(p>(Z+=SSq{)BejT;}IV|7*r(#vPbDm2yJ=wuxk+zXAgj9h(Dfljh!QD=ZjV zr~iR9wn=S0t;H|>u?G8hi0}0uno5iG_hw5EtB=ZsOzGyS`q$~0*DilgbV#{D>ly}V`Q`EjsqwF#Q zYu{rsG`}(HHMXsgEe?6@@*CUt^W-jFrti-}rr3}EG_H-(mF%E4WFTZ9WFTZ9WMCFD zU?XGkHB-K+?XhlFa@NehO)ENv&9m62xUlO5=bPeo4ow($jcbq3v!l7V?tR78t9{w^ zAM-XoZ}?y52V=i?!1;r*A2^VYbo7nnV*gcCA2oZ%d27pdV!bX7?l_L?2D<~gAiSqV z5`u*c9D4>%rRyn>+|SXzf}ZhS01M!5NZLXwS3@&Idl0UE3w{Zl0mo{3Uj7sK=ioX| z<30g;K7A&f1u|)pT?%)>2dX$b*ZB>2ol}cxT#f0+(7BpNn+gf@@%4OL?|DRf0g2=K zD)L$r%~KXn=jdjT=8IiV<*yTRuI`~ryd_lWhya!6M$kyIL*cg{pAI*dO4r3~CxSwOf9^pT> Cin5RZ literal 0 HcmV?d00001 diff --git a/docs/assets/vector-logo.svg b/docs/assets/vector-logo.svg new file mode 100644 index 0000000..8dd76b5 --- /dev/null +++ b/docs/assets/vector-logo.svg @@ -0,0 +1,172 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/index.md b/docs/index.md new file mode 100644 index 0000000..bad1009 --- /dev/null +++ b/docs/index.md @@ -0,0 +1,130 @@ +# SONIC-O1 Multi-Agent System + +**A compound multi-agent system for audio-video understanding with Qwen3-Omni.** + +SONIC-O1 is **not a single model** -- it is a **coordinated system of specialized agents** +that plan, reason, ground, and reflect to produce accurate, temporally-grounded answers +from video and audio content. + +--- + +## Demo + + + +> **[β–Ά Watch the full demo video](https://drive.google.com/file/d/1e1qv4JCKqeDc7UdZTIx1-c39zqXNKWtr/view?usp=sharing)** β€” Upload a video, ask a question, and watch the multi-agent pipeline analyze it in real-time with planning, temporal indexing, chain-of-thought reasoning, and self-reflection. + +The demo UI connects to a vLLM server and streams progress as each agent completes β€” see the [User Guide](user_guide.md#demo-ui) for setup instructions. + +--- + +## Key Features + +
+ +- :material-account-group: **Multi-Agent Coordination** + + --- + Specialized agents for planning, reasoning, and reflection work together + through a LangGraph workflow with conditional branching. + +- :material-brain: **Chain-of-Thought Reasoning** + + --- + Step-by-step analysis with explicit reasoning traces, self-verification + at each step, and confidence scoring. + +- :material-clock-outline: **Temporal Grounding** + + --- + Frame-captioning index splits video into segments, captions each with + time-sliced video and audio, and injects a timestamped index into + prompts for accurate second-level citations. + +- :material-magnify: **Self-Reflection** + + --- + Automatic quality assessment with iterative refinement until a + confidence threshold is met. Detects gaps and hallucinations. + +- :material-format-list-numbered: **Multi-Step Planning** + + --- + Automatic decomposition of complex queries into sub-tasks with + sequential execution and context passing. + +- :material-lightning-bolt: **Efficient Processing** + + --- + vLLM backend with tensor parallelism, prompt caching, smart video + segmentation, and per-segment audio/video slicing. Supports both + embedded inference and a decoupled server mode with parallel + temporal indexing. + +
+ +--- + +## Quick Start + +```bash +# Edit config with your model path +vim configs/agent_config.yaml + +# Edit SLURM script with your video/audio paths +vim slurm/run_sonic_agent_native.sh + +# Submit job +sbatch slurm/run_sonic_agent_native.sh + +# Monitor +tail -f logs/sonic_agent_*.out +``` + +See the [User Guide](user_guide.md) for detailed usage examples and +configuration options, or the [Architecture](architecture.md) page for +a deep dive into the multi-agent workflow and temporal grounding strategy. + +--- + +## Agent Modes + +| Mode | Agents Active | Use Case | Flag | +|------|--------------|----------|------| +| **Direct** | Planner + Temporal Index | Simple queries | *(default)* | +| **Reasoning** | Planner + Temporal Index + Reasoner | Complex analysis | `--reasoning` | +| **Reflective** | Planner + Temporal Index + Reflection | Quality-critical | `--reflection` | +| **Multi-Step** | Planner (Advanced) + Temporal Index | Comparisons, decomposition | `--multi-step` | +| **Full** | All agents | Maximum capability | `--all-features` | + +--- + +## Citation + +```bibtex +@software{sonic_o1_multi_agent, + author = {Radwan, Ahmed Y.}, + title = {Sonic O1: A Multi-Agent System for Audio-Video Understanding}, + year = {2026}, + publisher = {Vector Institute}, + note = {Compound AI system with planning, reasoning, and reflection agents} +} +``` + +--- + +## Acknowledgments + +Resources used in preparing this research were provided, in part, by the Province +of Ontario, the Government of Canada through CIFAR, and companies sponsoring the +Vector Institute. + +This research was funded by the European Union's Horizon Europe research and +innovation programme under the AIXPERT project (Grant Agreement No. 101214389). + +Built on: + +- [Qwen3-Omni](https://github.com/QwenLM/Qwen3-Omni) -- Multimodal foundation model +- [vLLM](https://github.com/vllm-project/vllm) -- Efficient LLM inference +- [LangGraph](https://github.com/langchain-ai/langgraph) -- Workflow orchestration +- [Vector Institute AI Engineering Template](https://github.com/VectorInstitute/aieng-template-uv) diff --git a/docs/overrides/partials/copyright.html b/docs/overrides/partials/copyright.html new file mode 100644 index 0000000..776166c --- /dev/null +++ b/docs/overrides/partials/copyright.html @@ -0,0 +1,22 @@ +
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+ + Vector Institute Logo + +
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+ {% endif %} {% if not config.extra.generator == false %} Made with + + Material for MkDocs + + {% endif %} +
diff --git a/docs/overrides/partials/logo.html b/docs/overrides/partials/logo.html new file mode 100644 index 0000000..2ed1c76 --- /dev/null +++ b/docs/overrides/partials/logo.html @@ -0,0 +1,5 @@ +{% if config.theme.logo %} +logo +{% else %} +{{ config.site_name }} +{% endif %} diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css new file mode 100644 index 0000000..4ab12ac --- /dev/null +++ b/docs/stylesheets/extra.css @@ -0,0 +1,91 @@ +[data-md-color-primary="vector"] { + --md-primary-fg-color: #eb088a; + --md-primary-fg-color--light: #f252a5; + --md-primary-fg-color--dark: #b00068; + --md-primary-bg-color: hsla(0, 0%, 100%, 1); + --md-primary-bg-color--light: hsla(0, 0%, 100%, 0.7); +} + +[data-md-color-primary="black"] { + --md-primary-fg-color: #181818; + --md-primary-fg-color--light: #f252a5; + --md-primary-fg-color--dark: #b00068; + --md-primary-bg-color: #eb088a; +} + +[data-md-color-accent="vector-teal"] { + --md-accent-fg-color: #48c0d9; + --md-accent-fg-color--transparent: #526cfe1a; + --md-accent-bg-color: #fff; + --md-accent-bg-color--light: #ffffffb3; +} + +[data-md-color-scheme="slate"][data-md-color-primary="black"] { + --md-typeset-a-color: #eb088a; +} + +[data-md-color-scheme="default"] { + /* Default light mode styling */ +} + +[data-md-color-scheme="slate"] { + --md-typeset-a-color: #eb088a; + /* Dark mode styling */ +} + +/* Vector logo css styling to match overrides/partial/copyright.html */ +.md-footer-vector { + display: flex; + align-items: center; + padding: 0 0.6rem; +} + +.md-footer-vector img { + height: 24px; /* Reduce height to a fixed value */ + width: auto; /* Maintain aspect ratio */ + transition: opacity 0.25s; + opacity: 0.7; +} + +.md-footer-vector img:hover { + opacity: 1; +} + +/* Make the inner footer grid elements distribute evenly */ +.md-footer-meta__inner { + display: flex; + justify-content: space-between; + align-items: center; +} + +/* To make socials and Vector logo not stack when viewing on mobile */ +@media screen and (max-width: 76.234375em) { + .md-footer-meta__inner.md-grid { + flex-direction: row; + justify-content: space-between; + align-items: center; + } + + .md-copyright, + .md-social { + width: auto; + max-width: 49%; + } + + /* Prevent margin that causes stacking */ + .md-social { + margin: 0; + } +} + +/* Reduce margins for h2 when using grid cards */ +.grid.cards h2 { + margin-top: 0; /* Remove top margin completely in cards */ + margin-bottom: 0.5rem; /* Smaller bottom margin in cards */ +} + +.vector-icon { + color: #eb088a; + opacity: 0.7; + margin-right: 0.2em; +} diff --git a/docs/user_guide.md b/docs/user_guide.md new file mode 100644 index 0000000..29f8aba --- /dev/null +++ b/docs/user_guide.md @@ -0,0 +1,367 @@ +# User Guide + +## Installation + +### Prerequisites + +- Python 3.10+ +- CUDA-capable GPUs (4x recommended for tensor parallelism) +- [uv](https://docs.astral.sh/uv/) package manager (optional; you can use pip in a container) + +### Setup + +```bash +# Clone the repository +git clone https://github.com/VectorInstitute/sonic-o1-agent.git +cd sonic-o1-agent + +# Install dependencies (with uv) +uv sync --all-extras --group docs --group test + +# Or with pip (e.g. inside a Singularity container) +pip install -e ".[docs]" # installs package + docs deps + +# Verify setup +python scripts/verify_setup.py +``` + +### Building the documentation + +From the project root, ensure the package is importable when MkDocs runs (required for API reference generation): + +```bash +# With uv +uv run mkdocs build + +# With system/conda Python: add src to PYTHONPATH +PYTHONPATH=src python -m mkdocs build +# Or: PYTHONPATH=src python -m mkdocs serve +``` + +--- + +## Usage Examples + +### Basic Inference (Direct Mode) + +The simplest mode uses the Planner and Temporal Index to produce a +grounded response in a single pass. + +```bash +python scripts/run_agent.py \ + --video courtroom.mp4 --audio courtroom.m4a \ + --query "Summarize the key arguments" +``` + +### Chain-of-Thought Reasoning + +Enables the Reasoner agent for step-by-step analysis with explicit +reasoning traces. + +```bash +python scripts/run_agent.py \ + --video hearing.mp4 \ + --query "Analyze the legal strategy used" \ + --reasoning +``` + +### Self-Reflection + +Enables the Reflection agent to evaluate response quality and +iteratively refine until the confidence threshold is met. + +```bash +python scripts/run_agent.py \ + --video deposition.mp4 \ + --query "What inconsistencies exist in the testimony?" \ + --reflection +``` + +### Multi-Step Decomposition + +Enables the advanced Planner to automatically decompose complex queries +into sequential sub-tasks. + +```bash +python scripts/run_agent.py \ + --video trial.mp4 \ + --query "Compare the defense attorney's arguments vs the prosecutor's" \ + --multi-step +``` + +### Full Multi-Agent System + +Enables all agents working together for maximum capability. + +```bash +python scripts/run_agent.py \ + --video complex_case.mp4 \ + --query "Provide a comprehensive analysis with key contradictions" \ + --all-features \ + --verbose \ + --output results.json +``` + +### Temporal Queries (Auto-Segmentation) + +The Planner agent automatically detects temporal references and segments +the video to process only the relevant window. + +```bash +python scripts/run_agent.py \ + --video hearing.mp4 \ + --query "What happened between minute 5 and 10?" +``` + +--- + +## Configuration + +All settings are in `configs/agent_config.yaml`. + +### Model Settings + +```yaml +model: + model_path: "Qwen/Qwen3-Omni-30B-A3B-Instruct" + use_thinking: false + + # vLLM backend mode (optional): + # Set to connect to a running vllm serve instance. + # Empty or omitted = embedded mode (loads model in-process). + # vllm_base_url: "http://localhost:8080/v1" + + gpu_memory_utilization: 0.85 + tensor_parallel_size: 4 # Number of GPUs + max_num_seqs: 1 + max_model_len: 65536 + + generation_config: + temperature: 0.0 # Greedy decoding + top_p: 0.95 + top_k: 20 + max_new_tokens: 8192 + + max_frames: 128 + min_frames: 64 +``` + +### Processing Settings + +```yaml +processing: + max_frames: 128 + max_audio_chunks: null # null = no chunking + audio_chunk_duration_sec: 10.0 + min_video_duration_for_segmentation: 300 # 5 minutes + segment_efficiency_threshold: 0.8 +``` + +### Temporal Index Settings + +Controls the frame-captioning index that provides temporal grounding. +See [Architecture -- Temporal Grounding](architecture.md#temporal-grounding) +for details on how this works. + +```yaml +temporal_index: + min_duration_sec: 180 # Skip indexing for short videos + num_segments: 10 # Segments to split video into + max_frames_per_segment: 16 # Frames per segment caption + caption_max_tokens: 256 # Max tokens per caption +``` + +!!! note "Short videos" + Videos shorter than `min_duration_sec` skip the temporal indexing step + entirely and rely on sampling-context hints instead. The model can + process the entire clip in a single pass without needing segment-level + grounding. + +### Reasoning Settings + +```yaml +reasoning: + max_reasoning_steps: 5 + enable_verification: true +``` + +### Reflection Settings + +```yaml +reflection: + confidence_threshold: 0.7 # Minimum confidence to accept + max_refinement_attempts: 2 + use_iterative_refinement: false # true = loop until ACCEPT or threshold + check_hallucination: false # true = run hallucination check after response +``` + +### Planning Settings + +```yaml +planning: + max_steps: 10 # Max sub-tasks when multi-step decomposes (e.g. compare/contrast) +``` + +--- + +## SLURM Submission + +For cluster environments, use the provided SLURM script: + +```bash +# Edit the script with your paths and resource requirements +vim slurm/run_sonic_agent_native.sh + +# Submit +sbatch slurm/run_sonic_agent_native.sh + +# Monitor output +tail -f logs/sonic_agent_*.out +``` + +--- + +## Demo UI + +> **[β–Ά Watch the demo video](https://drive.google.com/file/d/1e1qv4JCKqeDc7UdZTIx1-c39zqXNKWtr/view?usp=sharing)** + +The demo provides a web interface for uploading videos and querying the +multi-agent pipeline. It streams real-time progress as each agent +completes β€” planning, temporal indexing, inference, and reflection β€” with +a live thinking panel showing intermediate results. + +### Running the Demo + +The demo connects to a vLLM server via the OpenAI-compatible API. This +separates model serving from the application, enabling continuous batching +and parallel temporal indexing. + +**Step 1 β€” Start the vLLM server:** + +```bash +vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct \ + --port 8080 --host 0.0.0.0 \ + --dtype bfloat16 --max-model-len 32768 \ + --allowed-local-media-path / \ + -tp 1 +``` + +Adjust `-tp` to match your GPU count (e.g., `-tp 4` for 4 GPUs with +`--max-model-len 65536`). Wait for `Uvicorn running on http://0.0.0.0:8080`. + +**Step 2 β€” Start the demo server** (separate terminal, same node): + +```bash +PYTHONPATH=src python -c " +from sonic_o1_agent.api import serve +serve( + vllm_base_url='http://localhost:8080/v1', + port=8000, + config_path='configs/agent_config.yaml', +) +" +``` + +**Step 3 β€” Open in your browser:** + +``` +http://localhost:8000 +``` + +If running on a remote server or HPC cluster, forward the port: + +```bash +ssh -L 8000::8000 user@login-server +``` + +### Server Mode from CLI + +You can also use the vLLM server backend from the CLI (without the demo +UI). Add `vllm_base_url` to your config: + +```yaml +model: + vllm_base_url: "http://localhost:8080/v1" +``` + +Or pass it as an environment variable: + +```bash +VLLM_BASE_URL=http://localhost:8080/v1 python scripts/run_agent.py \ + --config configs/agent_config.yaml \ + --video video.mp4 --audio audio.m4a \ + --query "Summarize the key points" \ + --all-features +``` + +!!! info "Backward compatibility" + When `vllm_base_url` is empty or absent, the system uses the embedded + vLLM engine (loads model in-process). All existing CLI usage is + completely unchanged. + +--- + +## CLI Reference + +```text +usage: run_agent.py [-h] [--config CONFIG] [--video VIDEO] [--audio AUDIO] + --query QUERY [--max-frames MAX_FRAMES] + [--max-audio-chunks MAX_AUDIO_CHUNKS] + [--reasoning] [--reflection] [--multi-step] + [--all-features] [--output OUTPUT] [--verbose] [--stream] + +Arguments: + --config Path to config YAML (default: configs/agent_config.yaml) + --video Path to video file + --audio Path to audio file + --query Query/question to ask (required) + --max-frames Override max frames to process + --max-audio-chunks Override max audio chunks + +Agent Modes: + --reasoning Enable Chain-of-Thought reasoning + --reflection Enable self-reflection and refinement + --multi-step Enable multi-step task decomposition + --all-features Enable all advanced features + +Output: + --output Save results to JSON file + --verbose Show detailed reasoning trace + --stream Stream workflow progress (print each node as it runs; useful in job logs) +``` + +--- + +## Example Output + +```text +====================================================================== +SONIC-O1 AGENT RESPONSE +====================================================================== + +Query: Compare the defense attorney's arguments vs the prosecutor's +Mode: chain_of_thought + +Evidence: + video: + duration_sec: 922.22 + frames_analyzed: 128 + sampling_interval_sec: 7.26 + coverage_sec: [0.0, 922.19] + audio: + duration_sec: 922.3 + chunks_analyzed: 93 + chunk_duration_sec: 10.0 + coverage_sec: [0.0, 922.3] + +Reflection: + Confidence: 0.85 + Scores: {'completeness': 9, 'accuracy': 8, 'clarity': 9, 'evidence': 8} + +Response: + Around 30s, the defense attorney opens by... + Between 120s and 180s, the prosecutor counters with... + [Detailed comparative analysis with timestamped evidence...] + +====================================================================== +``` diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 0000000..a52a978 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,84 @@ +extra_css: + - stylesheets/extra.css +extra: + generator: false + social: + - icon: fontawesome/brands/discord + link: 404.html + - icon: fontawesome/brands/github + link: https://github.com/VectorInstitute/sonic-o1-agent +markdown_extensions: + - attr_list + - admonition + - md_in_html + - pymdownx.highlight: + anchor_linenums: true + line_spans: __span + pygments_lang_class: true + - pymdownx.inlinehilite + - pymdownx.details + - pymdownx.snippets + - pymdownx.superfences + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + - toc: + permalink: true +nav: + - Home: index.md + - User Guide: user_guide.md + - Architecture: architecture.md + - API Reference: api.md +plugins: + - search + - mkdocstrings: + handlers: + python: + options: + docstring_style: numpy + members_order: source + separate_signature: true + show_overloads: true + show_submodules: true + show_root_heading: false + show_root_full_path: true + show_root_toc_entry: false + show_symbol_type_heading: true + show_symbol_type_toc: true +repo_url: https://github.com/VectorInstitute/sonic-o1-agent +repo_name: VectorInstitute/sonic-o1-agent +site_name: SONIC-O1 Multi-Agent System +theme: + custom_dir: docs/overrides + favicon: assets/favicon-48x48.svg + features: + - content.code.annotate + - content.code.copy + - navigation.footer + - navigation.indexes + - navigation.instant + - navigation.tabs + - navigation.top + - search.suggest + - search.highlight + - toc.follow + icon: + repo: fontawesome/brands/github + logo: assets/vector-logo.svg + logo_footer: assets/vector-logo.svg + name: material + palette: + - media: "(prefers-color-scheme: light)" + scheme: default + primary: vector + accent: vector-teal + toggle: + icon: material/brightness-7 + name: Switch to dark mode + - media: "(prefers-color-scheme: dark)" + scheme: slate + primary: black + accent: vector-teal + toggle: + icon: material/brightness-4 + name: Switch to light mode diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..9561ddb --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,114 @@ +[project] +name = "sonic-o1-agent" +version = "0.1.0" +description = "Agentic audio-video processing with Qwen3-Omni" +readme = "README.md" +authors = [ + {name = "Ahmed Y. Radwan", email = "ahmed.radwan@vectorinstitute.ai"} +] +license = "Apache-2.0" +requires-python = ">=3.10" + +dependencies = [ + # Core ML/DL frameworks + "torch>=2.0.0", + "torchaudio>=2.0.0", + "torchvision>=0.15.0", + "transformers>=4.35.0", + "accelerate>=0.20.0", + + # vLLM for efficient inference (required for Qwen3-Omni) + "vllm>=0.5.0", + "langgraph>=0.2.0", + # Pin ray>=2.54.0 to address CVE-2026-27482 (audit fails on 2.53.0) + "ray>=2.54.0", + + # Qwen3-Omni specific + "qwen-omni-utils>=0.0.8", + + # Audio/Video processing + "av>=10.0.0", + "soundfile>=0.12.0", + "librosa>=0.10.0", + "opencv-python>=4.8.0", + "ffmpeg-python>=0.2.0", + "imageio>=2.0.0", + # Data handling + "numpy>=1.24.0", + "pandas>=2.0.0", + "pillow>=10.0.0", + # ML utilities + "einops>=0.8.0", + "timm>=0.9.0", + "safetensors>=0.4.0", + + # Configuration and logging + "pyyaml>=6.0.0", + "python-dotenv>=1.0.0", + + # Progress and utilities + "tqdm>=4.65.0", +] + +[project.optional-dependencies] +# Development tools +dev = [ + "pytest>=8.0.0", + "pytest-cov>=4.0.0", + "pytest-mock>=3.0.0", + "ruff>=0.3.0", + "mypy>=1.8.0", + "types-PyYAML>=6.0.0", + "pre-commit>=3.0.0", +] + +# Documentation +docs = [ + "mkdocs>=1.5.0", + "mkdocs-material>=9.0.0", + "mkdocstrings[python]>=0.24.0", +] + +# Full installation +all = [ + "sonic-o1-agent[dev,docs]", +] + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["src/sonic_o1_agent"] + +[tool.pytest.ini_options] +testpaths = ["tests/unit", "tests/integration"] +python_files = ["test_*.py"] +markers = [ + "slow: marks tests as slow (deselect with '-m \"not slow\"')", +] +filterwarnings = [ + "ignore:builtin type .* has no __module__ attribute:DeprecationWarning", +] + +[tool.ruff] +line-length = 88 +include = ["*.py", "pyproject.toml"] + +[tool.ruff.lint] +select = ["E", "F", "I", "W", "N"] +ignore = ["E501"] + +[tool.ruff.lint.per-file-ignores] +"__init__.py" = ["F401"] + +[tool.mypy] +python_version = "3.10" +warn_return_any = true +warn_unused_configs = true +disallow_untyped_defs = false +ignore_missing_imports = true + +[tool.coverage.run] +source = ["src/sonic_o1_agent"] +omit = ["tests/*", "*__init__.py"] diff --git a/scripts/run_agent.py b/scripts/run_agent.py new file mode 100644 index 0000000..e8a87bd --- /dev/null +++ b/scripts/run_agent.py @@ -0,0 +1,318 @@ +#!/usr/bin/env python3 +"""Entry point for Sonic O1 Agent. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import argparse +import json +import logging +import sys +from pathlib import Path + +import yaml + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +from sonic_o1_agent import SonicAgent + +# Setup logging +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", +) + +# Suppress verbose vLLM logging +logging.getLogger("vllm").setLevel(logging.WARNING) +logging.getLogger("vllm.engine").setLevel(logging.WARNING) +logging.getLogger("vllm.worker").setLevel(logging.WARNING) +logging.getLogger("vllm.multiproc_executor").setLevel(logging.WARNING) +logging.getLogger("vllm.parallel_state").setLevel(logging.WARNING) +logging.getLogger("vllm.config").setLevel(logging.WARNING) +logging.getLogger("vllm.engine.arg_utils").setLevel(logging.WARNING) + +logger = logging.getLogger(__name__) + + +def load_config(config_path: str) -> dict: + """Load configuration from YAML file.""" + with open(config_path, "r") as f: + return dict(yaml.safe_load(f)) + + +def _state_to_result(final_state: dict, video_path: str, audio_path: str) -> dict: + """Build the same result dict as SonicAgent.process() from merged state.""" + result = { + "response": final_state["response"], + "plan": final_state["plan"], + "reasoning_mode": final_state["reasoning_mode"], + "modalities_used": { + "video": video_path is not None, + "audio": audio_path is not None, + }, + } + if "reasoning_chain" in final_state: + result["reasoning_chain"] = final_state["reasoning_chain"] + result["confidence"] = final_state["reasoning_confidence"] + if "reasoning_trace" in final_state: + result["reasoning_trace"] = final_state["reasoning_trace"] + if "steps_executed" in final_state: + result["steps_executed"] = final_state["steps_executed"] + if "multi_step_plan" in final_state and final_state["multi_step_plan"]: + result["multi_step_plan"] = final_state["multi_step_plan"] + if "evidence" in final_state: + result["evidence"] = final_state["evidence"] + if "reflection" in final_state: + result["reflection"] = final_state["reflection"] + result["was_refined"] = final_state.get("was_refined", False) + if final_state.get("original_response"): + result["original_response"] = final_state["original_response"] + if "refinement_history" in final_state and final_state["refinement_history"]: + result["refinement_history"] = final_state["refinement_history"] + if "hallucination_assessment" in final_state: + result["hallucination_assessment"] = final_state["hallucination_assessment"] + return result + + +def _run_with_stream( + agent, + video_path: str, + audio_path: str, + query: str, + max_frames: int, + max_audio_chunks: int, + use_reasoning: bool, + use_reflection: bool, + use_multi_step: bool, +) -> dict: + """Run workflow with process_stream(); print progress and return result.""" + initial_state = { + "query": query, + "video_path": video_path, + "audio_path": audio_path, + "max_frames": max_frames, + "max_audio_chunks": max_audio_chunks, + "use_reasoning": use_reasoning, + "use_reflection": use_reflection, + "use_multi_step": use_multi_step, + "temp_files": [], + } + merged = dict(initial_state) + logger.info("Workflow progress (streaming):") + for event in agent.process_stream( + video_path=video_path, + audio_path=audio_path, + query=query, + max_frames=max_frames, + max_audio_chunks=max_audio_chunks, + use_reasoning=use_reasoning, + use_reflection=use_reflection, + use_multi_step=use_multi_step, + ): + node = event["node"] + state_update = event.get("state") + if state_update is not None and isinstance(state_update, dict): + merged = {**merged, **state_update} + # Progress line visible in sbatch log + print(f" β†’ {node}", flush=True) + logger.info(" β†’ %s", node) + return _state_to_result(merged, video_path, audio_path) + + +def main(): + """Run the Sonic O1 Agent.""" + parser = argparse.ArgumentParser( + description="Sonic O1 Agent - Multimodal Video/Audio Processing" + ) + parser.add_argument( + "--config", + type=str, + default="configs/agent_config.yaml", + help="Path to configuration file", + ) + parser.add_argument( + "--video", + type=str, + default=None, + help="Path to video file", + ) + parser.add_argument( + "--audio", + type=str, + default=None, + help="Path to audio file", + ) + parser.add_argument( + "--query", + type=str, + required=True, + help="Query/question to ask", + ) + parser.add_argument( + "--max-frames", + type=int, + default=None, + help="Maximum frames to process", + ) + parser.add_argument( + "--max-audio-chunks", + type=int, + default=None, + help="Maximum audio chunks to process", + ) + + # Advanced features + parser.add_argument( + "--reasoning", + action="store_true", + help="Enable Chain-of-Thought reasoning", + ) + parser.add_argument( + "--reflection", + action="store_true", + help="Enable self-reflection and refinement", + ) + parser.add_argument( + "--multi-step", + action="store_true", + help="Enable multi-step task decomposition", + ) + parser.add_argument( + "--all-features", + action="store_true", + help="Enable all advanced features (reasoning + reflection + multi-step)", + ) + + # Output options + parser.add_argument( + "--output", + type=str, + default=None, + help="Save results to JSON file", + ) + parser.add_argument( + "--verbose", + action="store_true", + help="Show detailed reasoning trace", + ) + parser.add_argument( + "--stream", + action="store_true", + help="Stream workflow progress (print each node as it runs); " + "shows in job log when using sbatch", + ) + + args = parser.parse_args() + + # Validate inputs + if args.video is None and args.audio is None: + parser.error("At least one of --video or --audio is required") + + # Load config + logger.info(f"Loading config from {args.config}") + config = load_config(args.config) + + # Handle --all-features flag + use_reasoning = args.reasoning or args.all_features + use_reflection = args.reflection or args.all_features + use_multi_step = args.multi_step or args.all_features + + # Initialize agent + logger.info("Initializing Sonic O1 Agent...") + agent = SonicAgent(config) + + try: + # Process query + logger.info(f"Processing query: {args.query}") + + if use_reasoning: + logger.info("β†’ Chain-of-Thought reasoning: ENABLED") + if use_reflection: + logger.info("β†’ Self-reflection: ENABLED") + if use_multi_step: + logger.info("β†’ Multi-step planning: ENABLED") + + if args.stream: + result = _run_with_stream( + agent=agent, + video_path=args.video, + audio_path=args.audio, + query=args.query, + max_frames=args.max_frames, + max_audio_chunks=args.max_audio_chunks, + use_reasoning=use_reasoning, + use_reflection=use_reflection, + use_multi_step=use_multi_step, + ) + else: + result = agent.process( + video_path=args.video, + audio_path=args.audio, + query=args.query, + max_frames=args.max_frames, + max_audio_chunks=args.max_audio_chunks, + use_reasoning=use_reasoning, + use_reflection=use_reflection, + use_multi_step=use_multi_step, + ) + + # Display results + print("\n" + "=" * 70) + print("SONIC O1 AGENT RESPONSE") + print("=" * 70) + print(f"\nQuery: {args.query}") + print(f"\nMode: {result.get('reasoning_mode', 'direct')}") + + # Show plan + if args.verbose: + print(f"\nPlan: {json.dumps(result['plan'], indent=2)}") + + # Show multi-step execution + if "multi_step_plan" in result and args.verbose: + print(f"\nMulti-Step Plan ({len(result['multi_step_plan'])} steps):") + for step in result["multi_step_plan"]: + print(f" {step['step_id']}. {step['description']}") + + # Show reasoning chain + if "reasoning_chain" in result and args.verbose: + print("\nReasoning Chain:") + for step in result["reasoning_chain"]: + print(f" Step {step['step']}: {step['action']}") + print(f" β†’ {step['thought'][:100]}...") + + if "reflection" in result: + r = result["reflection"] + confidence = r.get("final_confidence") or r.get("confidence") + lines = ["\nReflection:"] + if confidence is not None: + lines.append(f" Confidence: {confidence:.2f}") + if args.verbose: + if r.get("scores") is not None: + lines.append(f" Scores: {r['scores']}") + if result.get("was_refined"): + lines.append(" βœ“ Response was refined") + if r.get("total_attempts") is not None: + lines.append(f" Total refinement attempts: {r['total_attempts']}") + print("\n".join(lines)) + + # Show final response + print(f"\nResponse:\n{result['response']}") + print("\n" + "=" * 70) + + # Save to file if requested + if args.output: + output_path = Path(args.output) + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w") as f: + json.dump(result, f, indent=2) + + logger.info(f"Results saved to {args.output}") + + finally: + logger.info("Agent shutdown complete") + + +if __name__ == "__main__": + main() diff --git a/scripts/run_agent_interactive.py b/scripts/run_agent_interactive.py new file mode 100644 index 0000000..f339ce0 --- /dev/null +++ b/scripts/run_agent_interactive.py @@ -0,0 +1,475 @@ +#!/usr/bin/env python3 +"""Interactive CLI for Sonic O1 Agent. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import json +import logging +import sys +import warnings +from pathlib import Path +from typing import Optional + +import yaml + +# Add src to path +sys.path.insert(0, str(Path(__file__).parent.parent / "src")) + +from sonic_o1_agent import SonicAgent + +# Setup logging +logging.basicConfig( + level=logging.WARNING, # Only show WARNING and above by default + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", +) + +# Suppress verbose vLLM logging +logging.getLogger("vllm").setLevel(logging.WARNING) +logging.getLogger("vllm.engine").setLevel(logging.WARNING) +logging.getLogger("vllm.worker").setLevel(logging.WARNING) +logging.getLogger("vllm.multiproc_executor").setLevel(logging.WARNING) +logging.getLogger("vllm.parallel_state").setLevel(logging.WARNING) +logging.getLogger("vllm.config").setLevel(logging.WARNING) +logging.getLogger("vllm.engine.arg_utils").setLevel(logging.WARNING) + +# Suppress transformers warnings +logging.getLogger("transformers").setLevel(logging.ERROR) +warnings.filterwarnings("ignore", category=UserWarning, module="transformers") + +# Suppress workflow node INFO messages (keep only WARNING/ERROR) +logging.getLogger("sonic_o1_agent.workflows.nodes").setLevel(logging.WARNING) +logging.getLogger("sonic_o1_agent.agents.planner").setLevel(logging.WARNING) +logging.getLogger("sonic_o1_agent.agents.sonic_agent").setLevel(logging.WARNING) + +# Keep our own logger at INFO for important messages +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + + +class InteractiveCLI: + """Interactive command-line interface for Sonic O1 Agent.""" + + def __init__(self): + """Initialize interactive CLI.""" + self.agent = None + self.config = None + + def clear_screen(self): + """Clear terminal screen.""" + import os + + os.system("cls" if os.name == "nt" else "clear") + + def print_header(self): + """Print application header.""" + print("\n" + "=" * 70) + print("🎬 SONIC O1 MULTI-AGENT SYSTEM - Interactive Mode") + print("=" * 70) + print("Author: Ahmed Y. Radwan, SONIC-O1 Team") + print("=" * 70 + "\n") + + def get_yes_no(self, prompt: str, default: Optional[bool] = None) -> bool: + """Get yes/no input with validation. + + Args: + prompt: Prompt to display + default: Default value if user presses Enter (None = required) + + Returns: + True for yes, False for no + """ + while True: + if default is True: + prompt_text = f"{prompt} (Y/n): " + elif default is False: + prompt_text = f"{prompt} (y/N): " + else: + prompt_text = f"{prompt} (y/n): " + + response = input(prompt_text).strip().lower() + + # Handle default + if not response: + if default is not None: + return default + else: + print(" ❌ Please enter 'y' or 'n'") + continue + + # Validate input + if response in ["y", "yes"]: + return True + elif response in ["n", "no"]: + return False + else: + print(f" ❌ Invalid input '{response}'. Please enter 'y' or 'n'") + + def print_menu(self, title: str, options: list) -> str: + """Print menu and get user choice. + + Args: + title: Menu title + options: List of (key, description) tuples + + Returns: + Selected option key + """ + print(f"\n{title}") + print("-" * len(title)) + for key, description in options: + print(f" [{key}] {description}") + print() + + while True: + choice = input("Select option: ").strip().lower() + valid_keys = [k for k, _ in options] + if choice in valid_keys: + return choice + print(f" ❌ Invalid choice. Please select from: {', '.join(valid_keys)}") + + def get_file_path(self, file_type: str, optional: bool = False) -> Optional[str]: + """Get file path from user with validation. + + Args: + file_type: Type of file (video/audio) + optional: Whether file is optional + + Returns: + File path or None if skipped + """ + while True: + if optional: + path = input( + f"\n{file_type.capitalize()} file path (or press Enter to skip): " + ).strip() + if not path: + if self.get_yes_no(f"Skip {file_type}?"): + print(f" βœ“ Skipped {file_type}") + return None + else: + continue + else: + path = input(f"\n{file_type.capitalize()} file path: ").strip() + + if path: + file_path = Path(path) + if file_path.exists(): + print(f" βœ“ Found {file_type}: {file_path.name}") + return str(file_path) + else: + print(f" ❌ File not found: {path}") + if not self.get_yes_no("Try again?"): + if optional: + return None + else: + print("\n Exiting...") + sys.exit(1) + elif not optional: + print(" ❌ File path is required") + + def get_query(self) -> str: + """Get user query.""" + print("\n" + "=" * 70) + print("Enter your question about the video/audio:") + print("=" * 70) + query = input("\nQuery: ").strip() + + if not query: + print("❌ Query cannot be empty") + return self.get_query() + + return query + + def select_features(self) -> dict: + """Let user select which features to enable. + + Returns: + Dict with feature flags + """ + print("\n" + "=" * 70) + print("🧠 SELECT AGENT FEATURES") + print("=" * 70) + + features = {} + + # Quick modes + mode_options = [ + ("1", "Quick Mode (Direct inference - fastest)"), + ("2", "Smart Mode (with Chain-of-Thought reasoning)"), + ("3", "Quality Mode (with Self-Reflection)"), + ("4", "Advanced Mode (with Multi-Step Planning)"), + ("5", "Full Mode (All features enabled)"), + ("6", "Custom (Choose individual features)"), + ] + + mode = self.print_menu("Select Processing Mode:", mode_options) + + if mode == "1": + features = { + "reasoning": False, + "reflection": False, + "multi_step": False, + } + elif mode == "2": + features = { + "reasoning": True, + "reflection": False, + "multi_step": False, + } + elif mode == "3": + features = { + "reasoning": False, + "reflection": True, + "multi_step": False, + } + elif mode == "4": + features = { + "reasoning": False, + "reflection": False, + "multi_step": True, + } + elif mode == "5": + features = { + "reasoning": True, + "reflection": True, + "multi_step": True, + } + elif mode == "6": + # Custom selection + print("\nCustom Feature Selection:") + + features["reasoning"] = self.get_yes_no( + "Enable Chain-of-Thought reasoning?" + ) + features["reflection"] = self.get_yes_no("Enable Self-Reflection?") + features["multi_step"] = self.get_yes_no("Enable Multi-Step Planning?") + + # Display selected features + print("\nβœ“ Features enabled:") + if features["reasoning"]: + print(" β€’ Chain-of-Thought Reasoning") + if features["reflection"]: + print(" β€’ Self-Reflection") + if features["multi_step"]: + print(" β€’ Multi-Step Planning") + if not any(features.values()): + print(" β€’ Direct Inference (baseline)") + + return features + + def load_config(self, config_path: str = "configs/agent_config.yaml"): + """Load agent configuration.""" + print(f"\nπŸ“‹ Loading configuration from {config_path}...") + with open(config_path, "r") as f: + self.config = yaml.safe_load(f) + print("βœ“ Configuration loaded") + + def initialize_agent(self): + """Initialize the agent.""" + print("\nπŸš€ Initializing Sonic O1 Agent...") + # Suppress logs during initialization + workflow_logger = logging.getLogger("sonic_o1_agent.workflows.nodes") + original_level = workflow_logger.level + workflow_logger.setLevel(logging.WARNING) + + try: + self.agent = SonicAgent(self.config) + finally: + workflow_logger.setLevel(original_level) + + print("βœ“ Agent initialized (model will load on first query)") + + def process_query(self, video_path, audio_path, query, features): + """Process query with the agent. + + Args: + video_path: Path to video + audio_path: Path to audio + query: User query + features: Feature flags + + Returns: + Result dict + """ + print("\n" + "=" * 70) + print("🎬 PROCESSING...") + print("=" * 70) + print("(This may take a few minutes - model is processing your query)") + print() + + # Temporarily suppress INFO logs during processing + workflow_logger = logging.getLogger("sonic_o1_agent.workflows.nodes") + planner_logger = logging.getLogger("sonic_o1_agent.agents.planner") + agent_logger = logging.getLogger("sonic_o1_agent.agents.sonic_agent") + + original_levels = { + workflow_logger: workflow_logger.level, + planner_logger: planner_logger.level, + agent_logger: agent_logger.level, + } + + # Set to WARNING during processing + workflow_logger.setLevel(logging.WARNING) + planner_logger.setLevel(logging.WARNING) + agent_logger.setLevel(logging.WARNING) + + try: + result = self.agent.process( + video_path=video_path, + audio_path=audio_path, + query=query, + use_reasoning=features["reasoning"], + use_reflection=features["reflection"], + use_multi_step=features["multi_step"], + ) + finally: + # Restore original log levels + for logger_obj, level in original_levels.items(): + logger_obj.setLevel(level) + + return result + + def display_results(self, result: dict, verbose: bool = False): + """Display processing results. + + Args: + result: Result dict from agent + verbose: Show detailed information + """ + print("\n" + "=" * 70) + print("πŸ“Š RESULTS") + print("=" * 70) + + print(f"\nMode: {result.get('reasoning_mode', 'direct')}") + + # Show reflection if available + if "reflection" in result: + print("\nπŸ” Reflection:") + reflection = result["reflection"] + # Handle both iterative (final_confidence) and single (confidence) modes + confidence = reflection.get("final_confidence") or reflection.get( + "confidence" + ) + if confidence is not None: + print(f" Confidence: {confidence:.2%}") + if verbose and reflection.get("scores"): + print(f" Scores: {reflection['scores']}") + if reflection.get("total_attempts"): + print(f" Total refinement attempts: {reflection['total_attempts']}") + if result.get("was_refined"): + print(" βœ“ Response was refined for better quality") + + # Show multi-step plan if available + if "multi_step_plan" in result and verbose: + print(f"\nπŸ“‹ Multi-Step Plan ({len(result['multi_step_plan'])} steps):") + for step in result["multi_step_plan"]: + print(f" {step['step_id']}. {step['description']}") + + # Show reasoning chain if available + if "reasoning_chain" in result and verbose: + print("\n🧠 Reasoning Chain:") + for step in result["reasoning_chain"]: + print(f" Step {step['step']}: {step['action']}") + + # Final answer + print("\n" + "=" * 70) + print("πŸ’¬ ANSWER") + print("=" * 70) + print(f"\n{result['response']}\n") + print("=" * 70) + + def save_results(self, result: dict): + """Save results to file. + + Args: + result: Result dict + """ + if self.get_yes_no("Save results to file?"): + default_path = "data/outputs/interactive_result.json" + path = input(f"Output path (default: {default_path}): ").strip() + if not path: + path = default_path + + output_path = Path(path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + with open(output_path, "w") as f: + json.dump(result, f, indent=2) + + print(f"βœ“ Results saved to {output_path}") + + def run(self): + """Run interactive session.""" + self.clear_screen() + self.print_header() + + try: + # Load configuration + self.load_config() + + # Initialize agent + self.initialize_agent() + + # Main loop + while True: + print("\n" + "=" * 70) + print("πŸ“ INPUT FILES") + print("=" * 70) + + # Get file paths + video_path = self.get_file_path("video", optional=True) + audio_path = self.get_file_path("audio", optional=True) + + if not video_path and not audio_path: + print("\n❌ Error: At least one of video or audio is required") + if not self.get_yes_no("Try again?"): + break + continue + + # Get query + query = self.get_query() + + # Select features + features = self.select_features() + + # Ask for verbose output + verbose = self.get_yes_no("Show detailed information?") + + # Process + result = self.process_query(video_path, audio_path, query, features) + + # Display results + self.display_results(result, verbose=verbose) + + # Save results + self.save_results(result) + + # Continue? + print("\n" + "=" * 70) + if not self.get_yes_no("Process another query?"): + break + + print("\n" + "=" * 70) + print("πŸ‘‹ Thank you for using Sonic O1 Agent!") + print("=" * 70 + "\n") + + except KeyboardInterrupt: + print("\n\n⚠️ Interrupted by user") + except Exception as e: + print(f"\n❌ Error: {e}") + logger.exception("Error in interactive session") + finally: + if self.agent: + print("\nπŸ”„ Shutting down agent...") + print("βœ“ Agent shutdown complete") + + +def main(): + """Entry point.""" + cli = InteractiveCLI() + cli.run() + + +if __name__ == "__main__": + main() diff --git a/scripts/verify_setup.py b/scripts/verify_setup.py new file mode 100644 index 0000000..04fc460 --- /dev/null +++ b/scripts/verify_setup.py @@ -0,0 +1,222 @@ +#!/usr/bin/env python3 +"""Verify sonic-o1-agent setup and run basic tests. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import sys +from pathlib import Path + +SCRIPT_DIR = Path(__file__).parent +PROJECT_ROOT = SCRIPT_DIR.parent +sys.path.insert(0, str(PROJECT_ROOT / "src")) + +# Colors for terminal output +GREEN = "\033[92m" +RED = "\033[91m" +YELLOW = "\033[93m" +RESET = "\033[0m" +BOLD = "\033[1m" + + +def print_header(text): + """Print formatted header.""" + print(f"\n{BOLD}{'=' * 70}{RESET}") + print(f"{BOLD}{text}{RESET}") + print(f"{BOLD}{'=' * 70}{RESET}\n") + + +def print_success(text): + """Print success message.""" + print(f"{GREEN}βœ“{RESET} {text}") + + +def print_error(text): + """Print error message.""" + print(f"{RED}βœ—{RESET} {text}") + + +def print_warning(text): + """Print warning message.""" + print(f"{YELLOW}!{RESET} {text}") + + +def test_imports(): + """Test all imports work.""" + print("Testing imports...") + try: + print_success("All imports successful") + return True + except Exception as e: + print_error(f"Import failed: {e}") + return False + + +def test_agent_init(): + """Test agent initialization.""" + print("\nTesting agent initialization...") + try: + from sonic_o1_agent import SonicAgent + + config = { + "model": {"model_path": "test"}, + "processing": {"max_frames": 256}, + } + SonicAgent(config) + print_success("Agent initialized successfully") + return True + except Exception as e: + print_error(f"Agent init failed: {e}") + return False + + +def test_planner(): + """Test planner functionality.""" + print("\nTesting planner...") + try: + from sonic_o1_agent.agents.planner import AgentPlanner + + # Test time range parsing + query = "What happened between minute 5 and 10?" + time_range = AgentPlanner.parse_time_range(query) + assert time_range == (300, 600), "Time range parsing failed" + + print_success(f"Planner working: parsed '{query}' β†’ {time_range}") + return True + except Exception as e: + print_error(f"Planner test failed: {e}") + return False + + +def test_prompt_builder(): + """Test prompt builder.""" + print("\nTesting prompt builder...") + try: + from sonic_o1_agent.processors.prompt_builder import PromptBuilder + + query = "Summarize this video" + query_type = PromptBuilder.detect_query_type(query) + assert query_type == "summarization" + + prompt = PromptBuilder.build_prompt(query) + assert len(prompt) > 0 + + print_success(f"Prompt builder working: detected type '{query_type}'") + return True + except Exception as e: + print_error(f"Prompt builder test failed: {e}") + return False + + +def run_pytest(): + """Run pytest if available.""" + print("\nRunning pytest...") + try: + import pytest + + result = pytest.main( + [ + "tests/", + "-v", + "--tb=short", + "-m", + "not slow", + ] + ) + + if result == 0: + print_success("All pytest tests passed") + return True + else: + print_warning("Some pytest tests failed (check output above)") + return False + except ImportError: + print_warning("pytest not installed, skipping automated tests") + return True + except Exception as e: + print_error(f"pytest failed: {e}") + return False + + +def check_directory_structure(): + """Check directory structure is correct.""" + print("\nChecking directory structure...") + required_dirs = [ + "src/sonic_o1_agent/agents", + "src/sonic_o1_agent/models", + "src/sonic_o1_agent/core", + "src/sonic_o1_agent/processors", + "src/sonic_o1_agent/utils", + "configs", + "scripts", + "slurm", + "tests/unit", + "tests/integration", + ] + + all_exist = True + for dir_path in required_dirs: + path = Path(dir_path) + if path.exists(): + print_success(f"{dir_path}") + else: + print_error(f"{dir_path} - MISSING") + all_exist = False + + return all_exist + + +def main(): + """Run all verification tests.""" + print_header("Sonic O1 Agent - Setup Verification") + + results = [] + + # 1. Check directory structure + results.append(("Directory Structure", check_directory_structure())) + + # 2. Test imports + results.append(("Imports", test_imports())) + + # 3. Test agent initialization + results.append(("Agent Init", test_agent_init())) + + # 4. Test planner + results.append(("Planner", test_planner())) + + # 5. Test prompt builder + results.append(("Prompt Builder", test_prompt_builder())) + + # 6. Run pytest + results.append(("Pytest Suite", run_pytest())) + + # Summary + print_header("Verification Summary") + + all_passed = all(result for _, result in results) + + for name, passed in results: + if passed: + print_success(f"{name}: PASSED") + else: + print_error(f"{name}: FAILED") + + print() + + if all_passed: + print_success(f"{BOLD}All checks passed!{RESET}") + print() + print("Next steps:") + print(" 1. Edit slurm/run_sonic_agent.sh with your video/audio paths") + print(" 2. Submit job: sbatch slurm/run_sonic_agent.sh") + print(" 3. Monitor: tail -f logs/sonic_agent_*.out") + return 0 + else: + print_error(f"{BOLD}Some checks failed{RESET}") + print() + print("Please fix the issues above before proceeding.") + return 1 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/slurm/run_sonic_agent_native.sh b/slurm/run_sonic_agent_native.sh new file mode 100644 index 0000000..c5fb134 --- /dev/null +++ b/slurm/run_sonic_agent_native.sh @@ -0,0 +1,64 @@ +#!/bin/bash +#SBATCH --job-name=sonic_agent +#SBATCH --gres=gpu:a40:4 +#SBATCH --mem=128G +#SBATCH --time=1-00:00:00 +#SBATCH --cpus-per-task=16 +#SBATCH --output=logs/sonic_agent_%j.out +#SBATCH --mail-user=your.email@institution.edu + +# Load required modules (adjust based on your cluster) +module load cuda/12.1 +module load python/3.10 +module load ffmpeg/6.0 + +# Project paths - ADJUST THESE FOR YOUR SETUP +PROJ=/path/to/your/project +AGENT_DIR=${PROJ}/sonic-o1-agent +JOB_TMP=${PROJ}/tmp/job_${SLURM_JOB_ID} + +# Create temporary directories +mkdir -p "${JOB_TMP}"/{segments,cache,hf,torch,vllm} +mkdir -p "${JOB_TMP}/cache/torch/kernels" +chmod -R u+rwx "${JOB_TMP}" + +# Set environment variables +export TMPDIR=${JOB_TMP}/segments +export SCRATCH_DIR=${JOB_TMP} +export XDG_CACHE_HOME=${JOB_TMP}/cache +export HF_HOME=${JOB_TMP}/hf +export HUGGINGFACE_HUB_CACHE=${JOB_TMP}/hf/hub +export TRANSFORMERS_CACHE=${JOB_TMP}/hf +export TORCH_HOME=${JOB_TMP}/torch +export VLLM_CACHE_ROOT=${JOB_TMP}/vllm + +# vLLM settings +export VLLM_USE_SHM_BROADCAST=0 + +# Activate virtual environment (uncomment if using one) +# source ${AGENT_DIR}/.venv/bin/activate + +cd ${AGENT_DIR} + +echo '=========================================' +echo 'Sonic O1 Agent - Interactive Processing' +echo '=========================================' +echo "Job ID: ${SLURM_JOB_ID}" +echo "Running on: $(hostname)" +echo "GPUs: ${CUDA_VISIBLE_DEVICES}" +echo '=========================================' + +# Run the agent - ADJUST VIDEO/AUDIO PATHS +python scripts/run_agent.py \\ + --config configs/agent_config.yaml \\ + --video /path/to/your/video.mp4 \\ + --audio /path/to/your/audio.m4a \\ + --query "Summarize the key points discussed in this video" \\ + --stream + +echo '=========================================' +echo 'Processing completed!' +echo '=========================================' + +# Cleanup temporary files +rm -rf "${JOB_TMP}" diff --git a/src/sonic_o1_agent/__init__.py b/src/sonic_o1_agent/__init__.py new file mode 100644 index 0000000..71a35be --- /dev/null +++ b/src/sonic_o1_agent/__init__.py @@ -0,0 +1,7 @@ +"""Sonic O1 Agent - Audio-Video Processing Agent using Qwen3-Omni.""" + +__version__ = "0.1.0" + +from sonic_o1_agent.agents.sonic_agent import SonicAgent + +__all__ = ["SonicAgent"] diff --git a/src/sonic_o1_agent/agents/__init__.py b/src/sonic_o1_agent/agents/__init__.py new file mode 100644 index 0000000..49c192a --- /dev/null +++ b/src/sonic_o1_agent/agents/__init__.py @@ -0,0 +1,18 @@ +"""Agent components for planning and orchestration. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from sonic_o1_agent.agents.planner import AgentPlanner +from sonic_o1_agent.agents.planner_advanced import MultiStepPlanner +from sonic_o1_agent.agents.reasoner import ChainOfThoughtReasoner +from sonic_o1_agent.agents.reflection import SelfReflection +from sonic_o1_agent.agents.sonic_agent import SonicAgent + +__all__ = [ + "AgentPlanner", + "MultiStepPlanner", + "ChainOfThoughtReasoner", + "SelfReflection", + "SonicAgent", +] diff --git a/src/sonic_o1_agent/agents/planner.py b/src/sonic_o1_agent/agents/planner.py new file mode 100644 index 0000000..d60a81e --- /dev/null +++ b/src/sonic_o1_agent/agents/planner.py @@ -0,0 +1,204 @@ +"""Agent planning and task decomposition. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +import re +from pathlib import Path +from typing import Dict, Optional, Tuple + +logger = logging.getLogger(__name__) + + +class AgentPlanner: + """Plan and decompose tasks for the agent.""" + + @staticmethod + def parse_time_range(query: str) -> Optional[Tuple[float, float]]: + """Extract time range from user query. + + Args: + query: User's query text + + Returns: + Tuple of (start_seconds, end_seconds) or None + """ + query_lower = query.lower() + + # Pattern: "minute X to Y" or "minutes X to Y" + pattern1 = r"minute[s]?\s+(\d+)\s+to\s+(\d+)" + match = re.search(pattern1, query_lower) + if match: + start_min = int(match.group(1)) + end_min = int(match.group(2)) + return (start_min * 60, end_min * 60) + + # Pattern: "between minute X and Y" + pattern2 = r"between\s+minute[s]?\s+(\d+)\s+and\s+(\d+)" + match = re.search(pattern2, query_lower) + if match: + start_min = int(match.group(1)) + end_min = int(match.group(2)) + return (start_min * 60, end_min * 60) + + # Pattern: "at minute X" or "around minute X" + pattern3 = r"(?:at|around)\s+minute[s]?\s+(\d+)" + match = re.search(pattern3, query_lower) + if match: + target_min = int(match.group(1)) + # Create 1-minute window around the target + return (max(0, target_min * 60 - 30), (target_min * 60 + 30)) + + # Pattern: "first X minutes" or "last X minutes" + pattern4 = r"(first|last)\s+(\d+)\s+minute[s]?" + match = re.search(pattern4, query_lower) + if match: + position = match.group(1) + duration_min = int(match.group(2)) + if position == "first": + return (0, duration_min * 60) + # For "last", we'll need video duration - handled by caller + + return None + + @staticmethod + def should_segment_video( + video_path: Path, + query: str, + video_duration: Optional[float] = None, + ) -> Tuple[bool, Optional[Tuple[float, float]]]: + """Determine if video should be segmented. + + Args: + video_path: Path to video file + query: User's query + video_duration: Video duration in seconds (optional) + + Returns: + Tuple of (should_segment, time_range) + """ + # Parse time range from query + time_range = AgentPlanner.parse_time_range(query) + + if time_range is None: + return False, None + + # Get video duration if not provided + if video_duration is None: + try: + from sonic_o1_agent.core.multimodal_engine import ( + get_video_metadata, + ) + + metadata = get_video_metadata(str(video_path)) + video_duration = metadata.total_frames / metadata.fps + except Exception as e: + logger.warning(f"Could not get video duration: {e}") + return False, None + + # Handle "last X minutes" pattern + if "last" in query.lower() and time_range[0] == 0: + duration_requested = time_range[1] + time_range = ( + max(0, video_duration - duration_requested), + video_duration, + ) + + segment_duration = time_range[1] - time_range[0] + + # Only segment if: + # 1. Video is longer than 5 minutes AND + # 2. Segment is less than 80% of total duration + if video_duration > 300 and segment_duration < video_duration * 0.8: + logger.info( + f"Planning to segment video: {time_range[0]}s - " + f"{time_range[1]}s (from {video_duration}s total)" + ) + return True, time_range + + return False, None + + @staticmethod + def determine_modalities( + video_path: Optional[Path], + audio_path: Optional[Path], + ) -> Dict[str, bool]: + """Determine which modalities are available. + + Args: + video_path: Path to video file (optional) + audio_path: Path to audio file (optional) + + Returns: + Dict with 'has_video' and 'has_audio' flags + """ + has_video = video_path is not None and video_path.exists() + has_audio = audio_path is not None and audio_path.exists() + + return {"has_video": has_video, "has_audio": has_audio} + + @staticmethod + def plan_processing( + video_path: Optional[Path], + audio_path: Optional[Path], + query: str, + config: Optional[Dict] = None, + ) -> Dict: + """Create processing plan for the agent. + + Args: + video_path: Path to video file (optional) + audio_path: Path to audio file (optional) + query: User's query + config: Agent configuration + + Returns: + Processing plan dict with all necessary parameters + """ + config = config or {} + + # Determine modalities + modalities = AgentPlanner.determine_modalities(video_path, audio_path) + + # Check if segmentation needed + should_segment = False + time_range = None + if video_path and modalities["has_video"]: + should_segment, time_range = AgentPlanner.should_segment_video( + video_path, query + ) + + # Detect query type + from sonic_o1_agent.processors.prompt_builder import PromptBuilder + + query_type = PromptBuilder.detect_query_type(query) + + # Get video duration for temporal grounding + duration_seconds = None + if video_path and modalities["has_video"]: + try: + from sonic_o1_agent.core.multimodal_engine import ( + get_video_metadata, + ) + + meta = get_video_metadata(str(video_path)) + duration_seconds = round(meta.total_frames / meta.fps, 2) + except Exception: + pass + + # Build plan + plan = { + "modalities": modalities, + "query_type": query_type, + "should_segment": should_segment, + "time_range": time_range, + "max_frames": config.get("max_frames", 256), + "max_audio_chunks": config.get("max_audio_chunks", None), + "duration_seconds": duration_seconds, + "processing_strategy": "single_pass", # Can be extended later + } + + logger.info(f"Generated processing plan: {plan}") + + return plan diff --git a/src/sonic_o1_agent/agents/planner_advanced.py b/src/sonic_o1_agent/agents/planner_advanced.py new file mode 100644 index 0000000..366dbce --- /dev/null +++ b/src/sonic_o1_agent/agents/planner_advanced.py @@ -0,0 +1,287 @@ +"""Advanced multi-step planning and task decomposition. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + + +class MultiStepPlanner: + """Decomposes complex queries into sequential sub-tasks.""" + + def __init__(self, config: Optional[Dict] = None): + """Initialize multi-step planner. + + Args: + config: Planner configuration + """ + self.config = config or {} + self.max_steps = self.config.get("max_steps", 10) + + def should_decompose(self, query: str) -> bool: + """Determine if query needs decomposition. + + Args: + query: User query + + Returns: + True if query should be decomposed + """ + # Keywords that indicate complex queries + complex_keywords = [ + "compare", + "contrast", + "difference between", + "both", + "each", + "all", + "versus", + "vs", + "analyze", + "relationship between", + "how does", + "explain why", + ] + + query_lower = query.lower() + return any(keyword in query_lower for keyword in complex_keywords) + + def decompose_query(self, query: str) -> List[Dict[str, Any]]: + """Decompose query into sequential steps. + + Args: + query: Complex user query + + Returns: + List of step specifications + """ + query_lower = query.lower() + steps = [] + + # Pattern 1: Comparison queries + if any( + kw in query_lower + for kw in ["compare", "contrast", "difference", "versus", "vs"] + ): + steps = self._decompose_comparison(query) + + # Pattern 2: Sequential analysis + elif any(kw in query_lower for kw in ["first", "then", "after", "next"]): + steps = self._decompose_sequential(query) + + # Pattern 3: Multi-entity analysis + elif any(kw in query_lower for kw in ["both", "each", "all"]): + steps = self._decompose_multi_entity(query) + + # Pattern 4: Causal/explanatory + elif any( + kw in query_lower for kw in ["why", "how does", "what causes", "explain"] + ): + steps = self._decompose_causal(query) + + # Default: Single comprehensive step + else: + steps = [ + { + "step_id": 1, + "action": "analyze", + "description": "Comprehensive analysis", + "query": query, + } + ] + + return steps[: self.max_steps] # Limit number of steps + + def _decompose_comparison(self, query: str) -> List[Dict[str, Any]]: + """Decompose comparison query.""" + # Extract entities being compared (simplified) + # In production, use NER or LLM to extract entities + + steps = [ + { + "step_id": 1, + "action": "identify_entity_1", + "description": "Identify first entity/person/topic", + "query": f"Identify the first subject in: {query}", + }, + { + "step_id": 2, + "action": "analyze_entity_1", + "description": "Analyze first entity", + "query": "Analyze what was said/done by the first subject", + "depends_on": [1], + }, + { + "step_id": 3, + "action": "identify_entity_2", + "description": "Identify second entity/person/topic", + "query": f"Identify the second subject in: {query}", + }, + { + "step_id": 4, + "action": "analyze_entity_2", + "description": "Analyze second entity", + "query": "Analyze what was said/done by the second subject", + "depends_on": [3], + }, + { + "step_id": 5, + "action": "compare", + "description": "Compare and contrast both entities", + "query": query, + "depends_on": [2, 4], + }, + ] + + return steps + + def _decompose_sequential(self, query: str) -> List[Dict[str, Any]]: + """Decompose sequential analysis query.""" + steps = [ + { + "step_id": 1, + "action": "identify_sequence", + "description": "Identify sequence of events", + "query": "What is the sequence of events?", + }, + { + "step_id": 2, + "action": "analyze_each", + "description": "Analyze each event", + "query": query, + "depends_on": [1], + }, + { + "step_id": 3, + "action": "synthesize", + "description": "Synthesize overall answer", + "query": query, + "depends_on": [2], + }, + ] + + return steps + + def _decompose_multi_entity(self, query: str) -> List[Dict[str, Any]]: + """Decompose multi-entity query.""" + steps = [ + { + "step_id": 1, + "action": "identify_all", + "description": "Identify all entities", + "query": "Identify all relevant subjects/entities", + }, + { + "step_id": 2, + "action": "analyze_each", + "description": "Analyze each entity separately", + "query": query, + "depends_on": [1], + }, + { + "step_id": 3, + "action": "aggregate", + "description": "Aggregate results", + "query": query, + "depends_on": [2], + }, + ] + + return steps + + def _decompose_causal(self, query: str) -> List[Dict[str, Any]]: + """Decompose causal/explanatory query.""" + steps = [ + { + "step_id": 1, + "action": "observe", + "description": "Observe what happened", + "query": "What events/actions occurred?", + }, + { + "step_id": 2, + "action": "identify_causes", + "description": "Identify potential causes", + "query": "What could have caused this?", + "depends_on": [1], + }, + { + "step_id": 3, + "action": "explain", + "description": "Explain causal relationships", + "query": query, + "depends_on": [1, 2], + }, + ] + + return steps + + def execute_plan( + self, + steps: List[Dict[str, Any]], + executor_fn: Any, + video_path: Optional[str] = None, + audio_path: Optional[str] = None, + ) -> Dict[str, Any]: + """Execute multi-step plan. + + Args: + steps: List of step specifications + executor_fn: Function to execute each step + video_path: Path to video + audio_path: Path to audio + + Returns: + Dict with results from all steps + """ + results: dict[str, Any] = {} + step_outputs: dict[int, Any] = {} + + logger.info(f"Executing {len(steps)}-step plan") + + for step in steps: + step_id = step["step_id"] + logger.info(f"Step {step_id}/{len(steps)}: {step['description']}") + + # Build context from dependent steps + context = {} + if "depends_on" in step: + for dep_id in step["depends_on"]: + if dep_id in step_outputs: + context[f"step_{dep_id}"] = step_outputs[dep_id] + + # Execute step + try: + result = executor_fn( + query=step["query"], + video_path=video_path, + audio_path=audio_path, + context=context, + ) + + step_outputs[step_id] = result + results[f"step_{step_id}"] = { + "action": step["action"], + "description": step["description"], + "result": result, + } + + except Exception as e: + logger.error(f"Step {step_id} failed: {e}") + results[f"step_{step_id}"] = { + "action": step["action"], + "description": step["description"], + "error": str(e), + } + + # Final synthesis + final_result = step_outputs.get(len(steps), "") + + return { + "final_answer": final_result, + "steps_executed": results, + "total_steps": len(steps), + } diff --git a/src/sonic_o1_agent/agents/reasoner.py b/src/sonic_o1_agent/agents/reasoner.py new file mode 100644 index 0000000..f153c35 --- /dev/null +++ b/src/sonic_o1_agent/agents/reasoner.py @@ -0,0 +1,457 @@ +"""Chain-of-Thought reasoning module. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +from typing import Any, Dict, List, Optional + +logger = logging.getLogger(__name__) + + +class ChainOfThoughtReasoner: + """Implements multi-step reasoning with explicit thought chains.""" + + def __init__(self, model, config: Optional[Dict] = None): + """Initialize the reasoner. + + Args: + model: The underlying language model (Qwen3OmniModel) + config: Reasoning configuration + """ + self.model = model + self.config = config or {} + self.max_reasoning_steps = self.config.get("max_reasoning_steps", 5) + self.enable_verification = self.config.get("enable_verification", True) + + def reason( + self, + query: str, + video_path: Optional[str], + audio_path: Optional[str], + context: Optional[Dict] = None, + ) -> Dict[str, Any]: + """Execute chain-of-thought reasoning. + + Args: + query: User's query + video_path: Path to video file + audio_path: Path to audio file + context: Additional context + + Returns: + Dict with reasoning chain and final answer + """ + context = context or {} + reasoning_chain = [] + + # Step 1: Understand the query + logger.info("CoT Step 1: Understanding query intent") + understanding = self._understand_query(query, context) + reasoning_chain.append( + { + "step": 1, + "action": "understand_query", + "thought": understanding["thought"], + "result": understanding["intent"], + } + ) + + # Step 2: Plan the approach + logger.info("CoT Step 2: Planning analysis approach") + plan = self._plan_approach(query, understanding, context) + reasoning_chain.append( + { + "step": 2, + "action": "plan_approach", + "thought": plan["thought"], + "result": plan["strategy"], + } + ) + + # Step 3: Execute analysis + logger.info("CoT Step 3: Executing analysis") + analysis = self._execute_analysis(query, video_path, audio_path, plan, context) + reasoning_chain.append( + { + "step": 3, + "action": "execute_analysis", + "thought": analysis["thought"], + "result": analysis["findings"], + } + ) + + # Step 4: Verify and refine (if enabled) + if self.enable_verification: + logger.info("CoT Step 4: Verifying results") + verification = self._verify_result(query, analysis, context) + reasoning_chain.append( + { + "step": 4, + "action": "verify_result", + "thought": verification["thought"], + "result": verification["assessment"], + } + ) + + # Step 5: Refine if needed + if verification["needs_refinement"]: + logger.info("CoT Step 5: Refining answer") + refinement = self._refine_answer(query, analysis, verification, context) + reasoning_chain.append( + { + "step": 5, + "action": "refine_answer", + "thought": refinement["thought"], + "result": refinement["improved_answer"], + } + ) + final_answer = refinement["improved_answer"] + else: + final_answer = analysis["findings"] + else: + final_answer = analysis["findings"] + + return { + "final_answer": final_answer, + "reasoning_chain": reasoning_chain, + "confidence": self._estimate_confidence(reasoning_chain), + } + + def _understand_query(self, query: str, context: Dict) -> Dict[str, Any]: + """Step 1: Understand what the user is asking.""" + understanding_prompt = f"""Analyze this query and explain your understanding: + +Query: {query} + +Think step by step: +1. What is the user asking for? +2. What type of information do they need? +3. What modalities (video/audio) are most important? +4. Is this a simple or complex question? + +Provide your analysis.""" + + response_text, _ = self.model.generate( + video_path=None, + audio_path=None, + prompt=understanding_prompt, + max_frames=0, # No video needed for understanding + ) + + return { + "thought": response_text, + "intent": self._extract_intent(response_text), + } + + def _extract_intent(self, understanding: str) -> str: + """Extract the core intent from understanding. + + Uses normalized keyword matching so we don't rely on the LLM + using exact words (e.g. "summary" vs "summarize", "moment" vs "when"). + """ + text = understanding.lower().strip() + # Summarization: summary, overview, recap, etc. + if any( + kw in text + for kw in ( + "summarize", + "summarise", + "summary", + "overview", + "recap", + "sum up", + ) + ): + return "summarization" + # Temporal: when, time, timestamp, moment, duration, etc. + if any( + kw in text + for kw in ( + "when", + "time", + "timestamp", + "moment", + "at what point", + "duration", + "minute", + "second", + "temporal", + ) + ): + return "temporal_localization" + # Comparison: compare, contrast, difference, versus, etc. + if any( + kw in text + for kw in ( + "compare", + "comparison", + "contrast", + "difference", + "versus", + "vs.", + ) + ): + return "comparison" + # Analysis: analyze, examine, assess, look at, etc. + if any( + kw in text + for kw in ( + "analyze", + "analyse", + "analysis", + "examine", + "assess", + "look at", + "break down", + ) + ): + return "analysis" + return "general_qa" + + def _plan_approach( + self, query: str, understanding: Dict, context: Dict + ) -> Dict[str, Any]: + """Step 2: Plan how to approach the analysis.""" + intent = understanding["intent"] + + planning_prompt = f"""Given this query and understanding, plan your approach: + +Query: {query} +Intent: {intent} +Understanding: {understanding["thought"]} + +Plan step by step: +1. What should I focus on in the video? +2. What should I listen for in the audio? +3. Should I analyze the whole video or specific segments? +4. What's the best order to process information? + +Provide your plan.""" + + response_text, _ = self.model.generate( + video_path=None, + audio_path=None, + prompt=planning_prompt, + max_frames=0, + ) + + return { + "thought": response_text, + "strategy": self._extract_strategy(response_text), + } + + def _extract_strategy(self, plan: str) -> str: + """Extract the strategy from the plan. + + Uses normalized keyword matching so we don't rely on exact LLM wording. + """ + text = plan.lower().strip() + + focused_keywords = ( + "segment", + "specific part", + "portion", + "section", + "specific range", + "focus on", + ) + if any(kw in text for kw in focused_keywords): + return "focused_analysis" + + comparative_keywords = ( + "compare", + "comparison", + "contrast", + "difference", + "versus", + ) + if any(kw in text for kw in comparative_keywords): + return "comparative_analysis" + + return "comprehensive_analysis" + + def _execute_analysis( + self, + query: str, + video_path: Optional[str], + audio_path: Optional[str], + plan: Dict, + context: Dict, + ) -> Dict[str, Any]: + """Step 3: Execute the actual analysis.""" + max_frames = context.get("max_frames", 256) + max_audio_chunks = context.get("max_audio_chunks", None) + duration = context.get("duration_seconds") + + # Build temporal grounding preamble + grounding_parts: List[str] = [] + if max_frames and duration: + interval = round(duration / max(max_frames, 1), 1) + grounding_parts.append( + f"The video is {round(duration, 1)}s long. You are " + f"viewing {max_frames} uniformly sampled frames " + f"(one every ~{interval}s). The audio covers the " + f"full duration." + ) + elif max_frames: + grounding_parts.append( + f"You are viewing {max_frames} uniformly sampled frames from the video." + ) + + segment_start = context.get("segment_start") + segment_end = context.get("segment_end") + if segment_start is not None and segment_end is not None: + grounding_parts.append( + f"This content covers {segment_start}s - " + f"{segment_end}s of the original video." + ) + + grounding = "\n".join(grounding_parts) if grounding_parts else "" + + # Use the temporal index if one was built by the indexing node + temporal_index = context.get("temporal_index") + if temporal_index: + index_block = f"--- VIDEO INDEX ---\n{temporal_index}\n--- END INDEX ---" + analysis_prompt = ( + "IMPORTANT: A timestamped content index of the video " + "is provided below. Use it to cite accurate timestamps " + "in seconds when describing events. Format: " + "'Around 30s, ...' or 'Between 120s and 180s, ...'.\n\n" + f"{grounding}\n\n{index_block}\n\n" + f"Query: {query}\n\n" + "Analyze the video and audio content. For each key " + "point or event you identify:\n" + "1. State the time in seconds (from the index above)\n" + "2. Describe what is shown or said\n" + "3. Explain its significance\n\n" + "Structure your response chronologically." + ) + else: + analysis_prompt = ( + "IMPORTANT: Your response MUST include approximate " + "timestamps in seconds for every key point or event " + "you describe. Use the audio and visual progression " + "to estimate when things occur in the video. Format: " + "'Around 30s, ...' or 'Between 120s and 180s, ...'.\n\n" + f"{grounding}\n\n" + f"Query: {query}\n\n" + "Analyze the video and audio content. For each key " + "point or event you identify:\n" + "1. State the approximate time in seconds when it " + "occurs\n" + "2. Describe what is shown or said\n" + "3. Explain its significance\n\n" + "Structure your response chronologically, ordered by " + "when things appear in the video." + ) + + response_text, _metadata = self.model.generate( + video_path=video_path, + audio_path=audio_path, + prompt=analysis_prompt, + max_frames=max_frames, + max_audio_chunks=max_audio_chunks, + ) + + return { + "thought": "Analyzing video and audio content...", + "findings": response_text, + } + + def _verify_result( + self, query: str, analysis: Dict, context: Dict + ) -> Dict[str, Any]: + """Step 4: Verify the analysis is complete and accurate.""" + verification_prompt = f"""Review your analysis for completeness: + +Original Query: {query} +Your Analysis: {analysis["findings"]} + +Verify step by step: +1. Did you fully answer the question? +2. Is your answer supported by evidence from the video/audio? +3. Are there any contradictions or gaps? +4. What's your confidence level (0-1)? + +Provide your verification assessment.""" + + response_text, _ = self.model.generate( + video_path=None, + audio_path=None, + prompt=verification_prompt, + max_frames=0, + ) + + # Simple confidence extraction + needs_refinement = ( + "incomplete" in response_text.lower() + or "gap" in response_text.lower() + or "not fully" in response_text.lower() + ) + + return { + "thought": response_text, + "assessment": "needs_refinement" if needs_refinement else "complete", + "needs_refinement": needs_refinement, + } + + def _refine_answer( + self, query: str, analysis: Dict, verification: Dict, context: Dict + ) -> Dict[str, Any]: + """Step 5: Refine the answer based on verification.""" + refinement_prompt = f"""Improve your answer based on verification: + +Original Query: {query} +Previous Answer: {analysis["findings"]} +Verification Feedback: {verification["thought"]} + +Refine your answer: +1. Address any gaps identified +2. Add missing evidence +3. Clarify any contradictions +4. Provide a complete, improved answer + +Provide your refined answer.""" + + response_text, _ = self.model.generate( + video_path=None, + audio_path=None, + prompt=refinement_prompt, + max_frames=0, + ) + + return { + "thought": "Refining answer based on verification...", + "improved_answer": response_text, + } + + def _estimate_confidence(self, reasoning_chain: List[Dict]) -> float: + """Estimate confidence based on reasoning chain.""" + # Simple heuristic - can be improved + if len(reasoning_chain) >= 5: + return 0.9 # Went through full verification + elif len(reasoning_chain) >= 3: + return 0.75 # Basic reasoning + else: + return 0.6 # Minimal reasoning + + def get_reasoning_trace(self, result: Dict) -> str: + """Format reasoning chain as readable text. + + Args: + result: Result dict from reason() + + Returns: + Formatted reasoning trace + """ + trace = "# Reasoning Trace\n\n" + + for step_info in result["reasoning_chain"]: + trace += f"## Step {step_info['step']}: {step_info['action']}\n" + trace += f"**Thought:** {step_info['thought'][:200]}...\n" + trace += f"**Result:** {step_info['result']}\n\n" + + trace += f"**Final Confidence:** {result['confidence']:.2f}\n" + + return trace diff --git a/src/sonic_o1_agent/agents/reflection.py b/src/sonic_o1_agent/agents/reflection.py new file mode 100644 index 0000000..5464ee8 --- /dev/null +++ b/src/sonic_o1_agent/agents/reflection.py @@ -0,0 +1,360 @@ +"""Self-reflection and confidence estimation module. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +import re +from typing import Any, Dict, Optional + +logger = logging.getLogger(__name__) + + +class SelfReflection: + """Implements self-reflection and confidence assessment.""" + + def __init__(self, model, config: Optional[Dict] = None): + """Initialize self-reflection module. + + Args: + model: The underlying language model (Qwen3OmniModel) + config: Reflection configuration + """ + self.model = model + self.config = config or {} + self.confidence_threshold = self.config.get("confidence_threshold", 0.7) + self.max_refinement_attempts = self.config.get("max_refinement_attempts", 2) + + def evaluate_response( + self, + query: str, + response: str, + context: Optional[Dict] = None, + ) -> Dict[str, Any]: + """Evaluate response quality and confidence. + + Args: + query: Original user query + response: Generated response + context: Additional context + + Returns: + Dict with confidence score and assessment + """ + logger.info("Evaluating response quality...") + + evaluation_prompt = f"""Evaluate this response critically: + +Query: {query} +Response: {response} + +Evaluate on these criteria: +1. **Completeness**: Does it fully answer the question? (0-10) +2. **Accuracy**: Is the information correct and grounded? (0-10) +3. **Clarity**: Is it clear and well-structured? (0-10) +4. **Evidence**: Is it supported by video/audio evidence? (0-10) + +For each criterion, provide: +- Score (0-10) +- Brief justification + +Then provide: +- Overall confidence (0.0-1.0) +- Key strengths +- Key weaknesses (if any) +- Recommendation: [ACCEPT, REFINE, REJECT] + +Format your response as: +Completeness: [score] - [justification] +Accuracy: [score] - [justification] +Clarity: [score] - [justification] +Evidence: [score] - [justification] +Confidence: [0.0-1.0] +Strengths: [list] +Weaknesses: [list] +Recommendation: [ACCEPT/REFINE/REJECT] +""" + + evaluation, _ = self.model.generate( + video_path=None, + audio_path=None, + prompt=evaluation_prompt, + max_frames=0, + ) + + # Parse evaluation + parsed = self._parse_evaluation(evaluation) + + return { + "raw_evaluation": evaluation, + "confidence": parsed["confidence"], + "scores": parsed["scores"], + "strengths": parsed["strengths"], + "weaknesses": parsed["weaknesses"], + "recommendation": parsed["recommendation"], + } + + def _parse_evaluation(self, evaluation: str) -> Dict[str, Any]: + """Parse evaluation text into structured format.""" + scores = {} + confidence = 0.5 # Default + strengths = [] + weaknesses = [] + recommendation = "ACCEPT" + + # Extract scores + for criterion in ["Completeness", "Accuracy", "Clarity", "Evidence"]: + pattern = rf"{criterion}:\s*(\d+)" + match = re.search(pattern, evaluation, re.IGNORECASE) + if match: + scores[criterion.lower()] = int(match.group(1)) + + # Extract confidence + conf_pattern = r"Confidence:\s*(0?\.\d+|1\.0)" + conf_match = re.search(conf_pattern, evaluation, re.IGNORECASE) + if conf_match: + confidence = float(conf_match.group(1)) + else: + # Fallback: average of scores + if scores: + avg_score = sum(scores.values()) / len(scores) + confidence = avg_score / 10.0 + + # Extract strengths + strengths_pattern = r"Strengths?:\s*(.+?)(?=Weaknesses?:|Recommendation:|$)" + strengths_match = re.search( + strengths_pattern, evaluation, re.IGNORECASE | re.DOTALL + ) + if strengths_match: + strengths = [ + s.strip() + for s in strengths_match.group(1).split("\n") + if s.strip() and not s.strip().startswith("-") + ] + + # Extract weaknesses + weaknesses_pattern = r"Weaknesses?:\s*(.+?)(?=Recommendation:|$)" + weaknesses_match = re.search( + weaknesses_pattern, evaluation, re.IGNORECASE | re.DOTALL + ) + if weaknesses_match: + weaknesses = [ + w.strip() + for w in weaknesses_match.group(1).split("\n") + if w.strip() and not w.strip().startswith("-") + ] + + # Extract recommendation + rec_pattern = r"Recommendation:\s*(ACCEPT|REFINE|REJECT)" + rec_match = re.search(rec_pattern, evaluation, re.IGNORECASE) + if rec_match: + recommendation = rec_match.group(1).upper() + else: + # Fallback based on confidence + if confidence >= 0.8: + recommendation = "ACCEPT" + elif confidence >= 0.5: + recommendation = "REFINE" + else: + recommendation = "REJECT" + + return { + "confidence": confidence, + "scores": scores, + "strengths": strengths, + "weaknesses": weaknesses, + "recommendation": recommendation, + } + + def refine_response( + self, + query: str, + original_response: str, + evaluation: Dict[str, Any], + video_path: Optional[str] = None, + audio_path: Optional[str] = None, + max_frames: Optional[int] = None, + ) -> str: + """Refine response based on evaluation feedback. + + Args: + query: Original query + original_response: Initial response + evaluation: Evaluation results + video_path: Path to video (optional, for re-analysis) + audio_path: Path to audio (optional, for re-analysis) + max_frames: Max frames for re-analysis + + Returns: + Refined response + """ + logger.info("Refining response based on feedback...") + + weaknesses_text = "\n".join(f"- {w}" for w in evaluation["weaknesses"]) + + refinement_prompt = f"""Improve your previous response: + +Original Query: {query} +Previous Response: {original_response} + +Issues Identified: +{weaknesses_text} + +Confidence Score: {evaluation["confidence"]:.2f} + +Instructions: +1. Address each weakness identified above +2. Maintain the strengths from your previous response +3. Add missing details or evidence +4. Ensure completeness and accuracy +5. Provide a clear, well-structured answer + +Provide your improved response:""" + + refined, _ = self.model.generate( + video_path=video_path, + audio_path=audio_path, + prompt=refinement_prompt, + max_frames=max_frames or 128, # Re-analyze with fewer frames + ) + + return str(refined) + + def iterative_refinement( + self, + query: str, + initial_response: str, + video_path: Optional[str] = None, + audio_path: Optional[str] = None, + max_frames: Optional[int] = None, + ) -> Dict[str, Any]: + """Iteratively refine response until confidence threshold met. + + Args: + query: User query + initial_response: Initial response + video_path: Path to video + audio_path: Path to audio + max_frames: Max frames to process + + Returns: + Dict with final response and refinement history + """ + current_response = initial_response + refinement_history = [] + attempts = 0 + + while attempts < self.max_refinement_attempts: + # Evaluate current response + evaluation = self.evaluate_response(query, current_response) + + refinement_history.append( + { + "attempt": attempts + 1, + "response": current_response, + "confidence": evaluation["confidence"], + "recommendation": evaluation["recommendation"], + } + ) + + logger.info( + f"Refinement attempt {attempts + 1}: " + f"confidence={evaluation['confidence']:.2f}" + ) + + # Check if we should stop + if evaluation["recommendation"] == "ACCEPT": + logger.info("Response accepted") + break + + if evaluation["recommendation"] == "REJECT": + logger.warning("Response rejected, stopping refinement") + break + + if evaluation["confidence"] >= self.confidence_threshold: + logger.info("Confidence threshold met") + break + + # Refine + current_response = self.refine_response( + query, + current_response, + evaluation, + video_path, + audio_path, + max_frames, + ) + + attempts += 1 + + final_evaluation = self.evaluate_response(query, current_response) + + return { + "final_response": current_response, + "final_confidence": final_evaluation["confidence"], + "refinement_history": refinement_history, + "total_attempts": attempts + 1, + } + + def detect_hallucination( + self, + response: str, + video_path: Optional[str] = None, + audio_path: Optional[str] = None, + ) -> Dict[str, Any]: + """Detect potential hallucinations in response. + + Args: + response: Generated response + video_path: Path to video for verification + audio_path: Path to audio for verification + + Returns: + Dict with hallucination assessment + """ + logger.info("Checking for hallucinations...") + + hallucination_prompt = f"""Analyze this response for potential hallucinations: + +Response: {response} + +Check for: +1. Claims made without evidence from video/audio +2. Invented details not present in the source material +3. Contradictions or inconsistencies +4. Overly specific information that can't be verified + +For each potential hallucination found: +- Quote the problematic claim +- Explain why it's questionable +- Rate severity (LOW/MEDIUM/HIGH) + +If no hallucinations found, state: "No hallucinations detected" + +Provide your analysis:""" + + analysis, _ = self.model.generate( + video_path=None, # Rely only on the response text for hallucination detection + audio_path=None, # Rely only on the response text for hallucination detection + prompt=hallucination_prompt, + max_frames=0, # No video frames needed for hallucination detection + ) + + has_hallucination = "no hallucinations detected" not in analysis.lower() + + return { + "has_hallucination": has_hallucination, + "analysis": analysis, + "severity": self._extract_severity(analysis), + } + + def _extract_severity(self, analysis: str) -> str: + """Extract overall severity from hallucination analysis.""" + if "HIGH" in analysis: + return "HIGH" + elif "MEDIUM" in analysis: + return "MEDIUM" + elif "LOW" in analysis: + return "LOW" + else: + return "NONE" diff --git a/src/sonic_o1_agent/agents/sonic_agent.py b/src/sonic_o1_agent/agents/sonic_agent.py new file mode 100644 index 0000000..4791f4f --- /dev/null +++ b/src/sonic_o1_agent/agents/sonic_agent.py @@ -0,0 +1,212 @@ +"""Main Sonic O1 Agent with LangGraph integration. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +from pathlib import Path +from typing import Any, Dict, Iterator, List, Optional + +from sonic_o1_agent.workflows.graph import build_sonic_workflow + +logger = logging.getLogger(__name__) + + +class SonicAgent: + """Sonic O1 Agent with LangGraph workflow orchestration.""" + + def __init__(self, config: Dict[str, Any]): + """Initialize Sonic Agent with LangGraph. + + Args: + config: Configuration dictionary + """ + self.config = config + + # Build LangGraph workflow + logger.info("Building LangGraph workflow...") + workflow = build_sonic_workflow(config) + self.app = workflow.compile() + + logger.info("Sonic O1 Agent initialized with LangGraph") + + def process( + self, + video_path: Optional[str] = None, + audio_path: Optional[str] = None, + query: str = "", + max_frames: Optional[int] = None, + max_audio_chunks: Optional[int] = None, + use_reasoning: bool = False, + use_reflection: bool = False, + use_multi_step: bool = False, + ) -> Dict[str, Any]: + """Process video/audio with user query using LangGraph. + + Args: + video_path: Path to video file. + audio_path: Path to audio file. + query: User's query. + max_frames: Override max frames. + max_audio_chunks: Override max audio chunks. + use_reasoning: Enable Chain-of-Thought. + use_reflection: Enable self-reflection. + use_multi_step: Enable multi-step planning. + + Returns: + Dict with keys: response, plan, reasoning_mode, modalities_used, + and optionally reasoning_chain, reflection, steps_executed, etc. + + Raises: + ValueError: If query is empty or both video_path and audio_path + are None. + """ + if not query: + raise ValueError("Query cannot be empty") + + if video_path is None and audio_path is None: + raise ValueError("At least one of video_path or audio_path required") + + # Prepare initial state + initial_state: Dict[str, Any] = { + "query": query, + "video_path": video_path, + "audio_path": audio_path, + "max_frames": max_frames, + "max_audio_chunks": max_audio_chunks, + "use_reasoning": use_reasoning, + "use_reflection": use_reflection, + "use_multi_step": use_multi_step, + "temp_files": [], + } + + # Run workflow + logger.info("Running LangGraph workflow...") + final_state = self.app.invoke(initial_state) + + # Extract result + result = { + "response": final_state["response"], + "plan": final_state["plan"], + "reasoning_mode": final_state["reasoning_mode"], + "modalities_used": { + "video": video_path is not None, + "audio": audio_path is not None, + }, + } + + # Add optional fields + if "reasoning_chain" in final_state: + result["reasoning_chain"] = final_state["reasoning_chain"] + result["confidence"] = final_state["reasoning_confidence"] + if "reasoning_trace" in final_state: + result["reasoning_trace"] = final_state["reasoning_trace"] + + if "steps_executed" in final_state: + result["steps_executed"] = final_state["steps_executed"] + if "multi_step_plan" in final_state and final_state["multi_step_plan"]: + result["multi_step_plan"] = final_state["multi_step_plan"] + if "evidence" in final_state: + result["evidence"] = final_state["evidence"] + + if "reflection" in final_state: + result["reflection"] = final_state["reflection"] + result["was_refined"] = final_state.get("was_refined", False) + if final_state.get("original_response"): + result["original_response"] = final_state["original_response"] + if "refinement_history" in final_state and final_state["refinement_history"]: + result["refinement_history"] = final_state["refinement_history"] + if "hallucination_assessment" in final_state: + result["hallucination_assessment"] = final_state["hallucination_assessment"] + + logger.info("LangGraph workflow complete") + return result + + def process_stream( + self, + video_path: Optional[str] = None, + audio_path: Optional[str] = None, + query: str = "", + max_frames: Optional[int] = None, + max_audio_chunks: Optional[int] = None, + use_reasoning: bool = False, + use_reflection: bool = False, + use_multi_step: bool = False, + ) -> Iterator[Dict[str, Any]]: + """Run the workflow and stream progress events (node name + state update). + + Yields dicts with keys "node" (str) and "state" (partial state update). + After the last event, accumulate state to build the same result as + process(), or consume state["response"] as it appears after inference. + + Args: + video_path: Path to video file. + audio_path: Path to audio file. + query: User's query. + max_frames: Override max frames. + max_audio_chunks: Override max audio chunks. + use_reasoning: Enable Chain-of-Thought. + use_reflection: Enable self-reflection. + use_multi_step: Enable multi-step planning. + + Yields: + Dict with "node" (name of the node that just ran) and "state" + (that node's partial state update). + + Raises: + ValueError: If query is empty or both video_path and audio_path + are None. + """ + if not query: + raise ValueError("Query cannot be empty") + if video_path is None and audio_path is None: + raise ValueError("At least one of video_path or audio_path required") + initial_state: Dict[str, Any] = { + "query": query, + "video_path": video_path, + "audio_path": audio_path, + "max_frames": max_frames, + "max_audio_chunks": max_audio_chunks, + "use_reasoning": use_reasoning, + "use_reflection": use_reflection, + "use_multi_step": use_multi_step, + "temp_files": [], + } + for event in self.app.stream( + initial_state, + stream_mode="updates", + ): + for node_name, state_update in event.items(): + yield {"node": node_name, "state": state_update} + + def _clean_temp_files(self, paths: List[str]) -> None: + """Remove temporary files (e.g. from segmentation). Safe if missing.""" + for path in paths: + if not path: + continue + try: + p = Path(path) + if p.exists(): + p.unlink(missing_ok=True) + logger.debug("Cleaned temp file: %s", path) + except OSError as e: + logger.warning("Failed to clean temp file %s: %s", path, e) + + def get_model_info(self) -> Dict[str, Any]: + """Return backend and workflow info. + + Returns: + Dict with keys "backend" and "workflow". + """ + return { + "backend": "LangGraph + vLLM", + "workflow": "enabled", + } + + def __enter__(self) -> "SonicAgent": + """Enter context manager. Returns self.""" + return self + + def __exit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None: + """Exit context manager. No-op.""" + pass diff --git a/src/sonic_o1_agent/api.py b/src/sonic_o1_agent/api.py new file mode 100644 index 0000000..e0179f9 --- /dev/null +++ b/src/sonic_o1_agent/api.py @@ -0,0 +1,385 @@ +"""FastAPI server for sonic-o1-agent demo. + +Serves the demo UI and proxies inference to a vLLM server running +Qwen3-Omni. The user is expected to start the vLLM server separately +(e.g. ``vllm serve Qwen/Qwen3-Omni-30B-A3B-Instruct ...``) and then +run ``sonic-o1-agent serve --vllm-url `` to launch this demo. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import json +import logging +import os +import shutil +import subprocess +import time +import uuid +from contextlib import asynccontextmanager +from pathlib import Path +from typing import Any, AsyncGenerator, Dict, Generator + +import uvicorn +import yaml +from fastapi import FastAPI, File, Form, HTTPException, Request, UploadFile +from fastapi.responses import HTMLResponse, StreamingResponse +from fastapi.staticfiles import StaticFiles +from pydantic import BaseModel + +logger = logging.getLogger(__name__) + +DEMO_DIR = Path(__file__).parent / "demo" + +# --------------------------------------------------------------------------- +# Environment / CLI configuration +# --------------------------------------------------------------------------- +VLLM_BASE_URL = os.environ.get("VLLM_BASE_URL", "") +MAX_VIDEO_DURATION = int(os.environ.get("MAX_VIDEO_DURATION", "1800")) # 30 min default +MAX_UPLOAD_MB = int(os.environ.get("MAX_UPLOAD_MB", "500")) + +# Upload directory β€” must be on a shared filesystem visible to BOTH +# the demo server and the vLLM server (they may be in separate containers). +# Falls back to a project-level path; override via SONIC_UPLOAD_DIR env var. +_default_upload_dir = os.environ.get( + "SONIC_UPLOAD_DIR", + str(Path.home() / ".sonic_o1_uploads"), +) +UPLOAD_DIR = Path(_default_upload_dir) +UPLOAD_DIR.mkdir(parents=True, exist_ok=True) + + +# --------------------------------------------------------------------------- +# Response models +# --------------------------------------------------------------------------- +class HealthResponse(BaseModel): + status: str + model: str + vllm_url: str + + +# --------------------------------------------------------------------------- +# Application lifespan +# --------------------------------------------------------------------------- +@asynccontextmanager +async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]: + """Initialise the SonicAgent on startup.""" + vllm_url = getattr(app.state, "vllm_base_url", VLLM_BASE_URL) + config_path = getattr(app.state, "config_path", "configs/agent_config.yaml") + + # Load config + if Path(config_path).exists(): + with open(config_path) as f: + config = yaml.safe_load(f) + else: + logger.warning("Config %s not found, using defaults", config_path) + config = {} + + app.state.config = config + app.state.vllm_base_url = vllm_url + + # Build the SonicAgent (lazy – model loads on first request via vLLM server) + try: + from sonic_o1_agent.agents.sonic_agent import SonicAgent + + # Inject vLLM server URL into the config so the model wrapper + # uses the OpenAI-compatible HTTP endpoint instead of embedded vLLM. + model_cfg = config.setdefault("model", {}) + model_cfg["vllm_base_url"] = vllm_url + + app.state.agent = SonicAgent(config) + logger.info("SonicAgent initialised (vLLM @ %s)", vllm_url) + except Exception as e: + logger.warning("Could not initialise SonicAgent: %s", e) + app.state.agent = None + + yield + + # Cleanup uploads + if UPLOAD_DIR.exists(): + shutil.rmtree(UPLOAD_DIR, ignore_errors=True) + app.state.agent = None + + +# --------------------------------------------------------------------------- +# FastAPI app +# --------------------------------------------------------------------------- +app = FastAPI( + title="Sonic O1 Agent – Demo", + description=( + "Multi-agent video/audio understanding: planning, reasoning, " + "reflection, and temporal grounding powered by Qwen3-Omni." + ), + version="0.1.0", + lifespan=lifespan, +) + +if (DEMO_DIR / "static").exists(): + app.mount("/static", StaticFiles(directory=DEMO_DIR / "static"), name="static") + + +# --------------------------------------------------------------------------- +# Routes +# --------------------------------------------------------------------------- +@app.get("/", response_class=HTMLResponse) +def index() -> str: + html_file = DEMO_DIR / "templates" / "index.html" + if not html_file.exists(): + raise HTTPException(404, "Demo UI not found.") + return html_file.read_text() + + +@app.get("/health", response_model=HealthResponse) +def health(request: Request) -> HealthResponse: + agent = getattr(request.app.state, "agent", None) + vllm_url = getattr(request.app.state, "vllm_base_url", "") + if agent is not None: + return HealthResponse( + status="ok", model="Qwen3-Omni-30B-A3B", vllm_url=vllm_url + ) + return HealthResponse(status="starting", model="not loaded", vllm_url=vllm_url) + + +@app.post("/analyze/stream") +async def analyze_stream( + request: Request, + video: UploadFile = File(...), + query: str = Form(...), +) -> StreamingResponse: + """Upload video + query, stream node-by-node progress via SSE.""" + + agent = getattr(request.app.state, "agent", None) + if agent is None: + raise HTTPException(503, "Agent not ready.") + + if not query.strip(): + raise HTTPException(422, "Query cannot be empty.") + + # --- Save uploaded file ------------------------------------------------ + session_id = uuid.uuid4().hex[:12] + session_dir = UPLOAD_DIR / session_id + session_dir.mkdir(parents=True, exist_ok=True) + + video_path = session_dir / video.filename + with open(video_path, "wb") as f: + shutil.copyfileobj(video.file, f) + + file_mb = video_path.stat().st_size / (1024 * 1024) + if file_mb > MAX_UPLOAD_MB: + shutil.rmtree(session_dir, ignore_errors=True) + raise HTTPException( + 422, f"File too large: {file_mb:.0f} MB (max {MAX_UPLOAD_MB} MB)." + ) + + # --- Extract audio via ffmpeg ------------------------------------------ + audio_path = session_dir / "audio.wav" + try: + subprocess.run( + [ + "ffmpeg", + "-y", + "-i", + str(video_path), + "-vn", + "-acodec", + "pcm_s16le", + "-ar", + "16000", + "-ac", + "1", + str(audio_path), + ], + capture_output=True, + timeout=120, + check=True, + ) + except Exception as e: + logger.warning("Audio extraction failed: %s β€” continuing video-only", e) + audio_path = None + + # --- Check video duration ---------------------------------------------- + try: + probe = subprocess.run( + [ + "ffprobe", + "-v", + "error", + "-show_entries", + "format=duration", + "-of", + "default=noprint_wrappers=1:nokey=1", + str(video_path), + ], + capture_output=True, + text=True, + timeout=30, + ) + duration = float(probe.stdout.strip()) + if duration > MAX_VIDEO_DURATION: + shutil.rmtree(session_dir, ignore_errors=True) + raise HTTPException( + 422, + f"Video too long: {duration:.0f}s (max {MAX_VIDEO_DURATION}s).", + ) + except HTTPException: + raise + except Exception: + duration = None # couldn't probe β€” allow through + + # --- Stream inference --------------------------------------------------- + def event_stream() -> Generator[str, None, None]: + try: + start = time.time() + accumulated_state: Dict[str, Any] = {} + + audio_str = str(audio_path) if audio_path and audio_path.exists() else None + + for event in agent.process_stream( + video_path=str(video_path), + audio_path=audio_str, + query=query, + use_reasoning=True, + use_reflection=True, + use_multi_step=True, + ): + node = event["node"] + state_update = event["state"] + + # Guard: some nodes (e.g. cleanup) may return None or empty + if not state_update: + state_update = {} + + # Accumulate state from each node + accumulated_state.update(state_update) + + # Send node progress event + progress: Dict[str, Any] = { + "node": node, + "elapsed": round(time.time() - start, 1), + } + + # Send FULL node outputs for live thinking panel + if node == "planning": + if "plan" in state_update: + progress["plan"] = state_update["plan"] + if "multi_step_plan" in state_update: + progress["multi_step_plan"] = state_update["multi_step_plan"] + if "query_type" in state_update: + progress["query_type"] = state_update["query_type"] + + if node == "temporal_indexing" and "temporal_index" in state_update: + ti = state_update.get("temporal_index") + progress["temporal_segments"] = ti.count("\n") + 1 if ti else 0 + progress["temporal_index"] = ti # full caption text + + if node in ("direct", "reasoning", "multi_step"): + if "response" in state_update: + progress["response"] = state_update["response"] # full response + if "reasoning_chain" in state_update: + progress["reasoning_chain"] = state_update["reasoning_chain"] + if "reasoning_trace" in state_update: + progress["reasoning_trace"] = state_update["reasoning_trace"] + if "steps_executed" in state_update: + progress["steps_executed"] = state_update["steps_executed"] + if "evidence" in state_update: + progress["evidence"] = state_update["evidence"] + + if node == "reflection": + if "reflection" in state_update: + progress["reflection"] = state_update["reflection"] + if "hallucination_assessment" in state_update: + progress["hallucination_assessment"] = state_update[ + "hallucination_assessment" + ] + if "was_refined" in state_update: + progress["was_refined"] = state_update["was_refined"] + + yield "data: " + json.dumps(progress) + "\n\n" + + # --- Build final result from accumulated state ------------------ + result: Dict[str, Any] = { + "response": accumulated_state.get("response", ""), + "plan": accumulated_state.get("plan", {}), + "reasoning_mode": accumulated_state.get("reasoning_mode", "full"), + "modalities_used": {"video": True, "audio": audio_str is not None}, + } + + # Optional fields + for key in [ + "reasoning_chain", + "reasoning_confidence", + "reasoning_trace", + "steps_executed", + "multi_step_plan", + "evidence", + "reflection", + "was_refined", + "original_response", + "refinement_history", + "hallucination_assessment", + ]: + if key in accumulated_state: + result[key] = accumulated_state[key] + + if "reasoning_confidence" in accumulated_state: + result["confidence"] = accumulated_state["reasoning_confidence"] + + yield "data: " + json.dumps({"result": result}) + "\n\n" + + except Exception as e: + logger.error("Stream error: %s", e, exc_info=True) + yield "data: " + json.dumps({"error": str(e)}) + "\n\n" + finally: + try: + shutil.rmtree(session_dir, ignore_errors=True) + except Exception: + pass + + return StreamingResponse( + event_stream(), + media_type="text/event-stream", + headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"}, + ) + + +# --------------------------------------------------------------------------- +# Serve CLI entry point +# --------------------------------------------------------------------------- +def serve( + host: str = "0.0.0.0", + port: int = 8000, + vllm_base_url: str = "", + config_path: str = "configs/agent_config.yaml", + max_video_duration: int = 300, + reload: bool = False, +) -> None: + """Start the Sonic O1 Agent demo server. + + Parameters + ---------- + host : str + Bind address. + port : int + Bind port. + vllm_base_url : str + URL of the running vLLM server (e.g. http://localhost:8080/v1). + config_path : str + Path to agent_config.yaml. + max_video_duration : int + Maximum video duration in seconds. + reload : bool + Enable auto-reload for development. + """ + global MAX_VIDEO_DURATION + MAX_VIDEO_DURATION = max_video_duration + + app.state.vllm_base_url = vllm_base_url + app.state.config_path = config_path + + print(f"{'=' * 60}") + print(" 🎬 Sonic O1 Agent β€” Demo Server") + print(f" http://localhost:{port}") + if vllm_base_url: + print(f" vLLM backend: {vllm_base_url}") + print(f"{'=' * 60}") + + uvicorn.run(app, host=host, port=port, reload=reload) diff --git a/src/sonic_o1_agent/core/__init__.py b/src/sonic_o1_agent/core/__init__.py new file mode 100644 index 0000000..5bb84b2 --- /dev/null +++ b/src/sonic_o1_agent/core/__init__.py @@ -0,0 +1,24 @@ +"""Core multimodal processing engines. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +# Import from multimodal_engine for backward compatibility +# All exports are re-exported from the split modules +from sonic_o1_agent.core.multimodal_engine import ( + SAMPLE_RATE, + VideoMetadata, + fetch_video_pyav, + get_video_metadata, + load_audio_pyav, + process_mm_info, +) + +__all__ = [ + "load_audio_pyav", + "fetch_video_pyav", + "get_video_metadata", + "process_mm_info", + "VideoMetadata", + "SAMPLE_RATE", +] diff --git a/src/sonic_o1_agent/core/audio_processor.py b/src/sonic_o1_agent/core/audio_processor.py new file mode 100644 index 0000000..904eec3 --- /dev/null +++ b/src/sonic_o1_agent/core/audio_processor.py @@ -0,0 +1,198 @@ +"""Audio processing using PyAV. + +Memory-efficient audio loading with chunking and resampling. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +import time +from typing import Optional + +import av +import numpy as np + +from sonic_o1_agent.core.multimodal_utils import SAMPLE_RATE + +logger = logging.getLogger(__name__) + + +# ============================================================================ +# Audio Processing Functions +# ============================================================================ +def load_audio_pyav( + audio_path: str, + sr: int = SAMPLE_RATE, + offset: float = 0.0, + duration: Optional[float] = None, + max_duration: Optional[float] = None, + max_chunks: Optional[int] = None, + chunk_duration_sec: float = 10.0, +) -> np.ndarray: + """Memory-efficient audio loading using PyAV with optional chunking.""" + st = time.time() + + container = av.open(audio_path) + + if len(container.streams.audio) == 0: + container.close() + logger.info( + f" Audio: No audio stream found in {audio_path}, returning empty array" + ) + return np.array([], dtype=np.float32) + + audio_stream = container.streams.audio[0] + original_sr = audio_stream.sample_rate + + start_sample = int(offset * original_sr) + end_sample = int((offset + duration) * original_sr) if duration else None + + est_samples = int((duration or 60) * original_sr) + audio_buffer = np.zeros(est_samples, dtype=np.float32) + write_pos = 0 + + current_sample = 0 + for frame in container.decode(audio=0): + frame_data = frame.to_ndarray() + + if frame_data.ndim > 1: + frame_data = frame_data.mean(axis=0, dtype=np.float32) + else: + frame_data = frame_data.astype(np.float32) + + frame_samples = len(frame_data) + + if current_sample + frame_samples < start_sample: + current_sample += frame_samples + continue + + if end_sample and current_sample >= end_sample: + break + + frame_start = max(0, start_sample - current_sample) + frame_end = ( + min(frame_samples, end_sample - current_sample) + if end_sample + else frame_samples + ) + chunk = frame_data[frame_start:frame_end] + + chunk_len = len(chunk) + if write_pos + chunk_len > len(audio_buffer): + audio_buffer = np.resize(audio_buffer, write_pos + chunk_len + est_samples) + + audio_buffer[write_pos : write_pos + chunk_len] = chunk + write_pos += chunk_len + current_sample += frame_samples + + container.close() + + audio = audio_buffer[:write_pos] + + if len(audio) == 0: + logger.info( + f" Audio: No audio data decoded from {audio_path}, returning empty array" + ) + return np.array([], dtype=np.float32) + + # Resample if needed + if original_sr != sr: + ratio = sr / original_sr + new_length = int(len(audio) * ratio) + + if 0.9 < ratio < 1.1: + indices = (np.arange(new_length) / ratio).astype(np.int32) + indices = np.clip(indices, 0, len(audio) - 1) + audio = audio[indices] + else: + indices = np.linspace(0, len(audio) - 1, new_length) + audio = np.interp(indices, np.arange(len(audio)), audio) + + # Apply chunking if requested + if max_chunks is not None: + samples_per_chunk = int(chunk_duration_sec * sr) + total_samples = len(audio) + num_chunks = int(np.ceil(total_samples / samples_per_chunk)) + + if num_chunks > max_chunks: + logger.info( + f" Audio chunking: {total_samples} samples -> {num_chunks} chunks, sampling {max_chunks}" + ) + + chunks = [] + for i in range(num_chunks): + start_idx = i * samples_per_chunk + end_idx = min((i + 1) * samples_per_chunk, total_samples) + chunks.append(audio[start_idx:end_idx]) + + sample_indices = np.linspace(0, num_chunks - 1, max_chunks, dtype=int) + sampled_chunks = [chunks[i] for i in sample_indices] + audio = np.concatenate(sampled_chunks, axis=0) + + logger.info(f" Final audio: {len(audio)} samples (from {total_samples})") + + elif max_duration is not None: + max_samples = int(max_duration * sr) + if len(audio) > max_samples: + audio = audio[:max_samples] + + actual_duration = len(audio) / sr + + was_chunked = False + was_truncated = False + if max_chunks is not None: + samples_per_chunk = int(chunk_duration_sec * sr) + num_chunks = int(np.ceil(len(audio_buffer[:write_pos]) / samples_per_chunk)) + was_chunked = num_chunks > max_chunks + elif max_duration is not None: + was_truncated = actual_duration >= max_duration * 0.99 + + status = " (chunked)" if was_chunked else (" (truncated)" if was_truncated else "") + logger.info(f" Audio: {actual_duration:.2f}s{status} in {time.time() - st:.3f}s") + + return audio + + +def process_audio_with_metadata( + audio_path: str, + max_audio_duration: Optional[float] = None, + max_audio_chunks: Optional[int] = None, + audio_chunk_duration_sec: float = 10.0, + audio_start: Optional[float] = None, + audio_end: Optional[float] = None, +) -> tuple: + """Process audio and return audio + compact metadata summary. + + Supports optional ``audio_start`` / ``audio_end`` (seconds) to + restrict loading to a time range within the audio file. When + omitted the full audio is loaded. + + Metadata includes only aggregate stats (duration, chunk count, + chunk size, coverage range) -- no per-chunk sample indices. + """ + offset = audio_start if audio_start and audio_start > 0 else 0.0 + duration = (audio_end - offset) if audio_end is not None else None + + audio = load_audio_pyav( + audio_path, + sr=SAMPLE_RATE, + offset=offset, + duration=duration, + max_duration=max_audio_duration, + max_chunks=max_audio_chunks, + chunk_duration_sec=audio_chunk_duration_sec, + ) + + total_duration = round(len(audio) / SAMPLE_RATE, 2) + chunk_size = int(audio_chunk_duration_sec * SAMPLE_RATE) + num_chunks = int(np.ceil(len(audio) / chunk_size)) if len(audio) > 0 else 0 + + metadata = { + "duration_sec": total_duration, + "sample_rate": SAMPLE_RATE, + "chunks_analyzed": num_chunks, + "chunk_duration_sec": audio_chunk_duration_sec, + "coverage_sec": [round(offset, 2), round(offset + total_duration, 2)], + } + + return audio, metadata diff --git a/src/sonic_o1_agent/core/multimodal_engine.py b/src/sonic_o1_agent/core/multimodal_engine.py new file mode 100644 index 0000000..42ad330 --- /dev/null +++ b/src/sonic_o1_agent/core/multimodal_engine.py @@ -0,0 +1,245 @@ +"""Multimodal processing engine using PyAV. + +Production-grade PyAV-based multimodal processing for video, audio, and image data. +Memory-efficient frame sampling and optimized audio loading with chunking. + +This module provides orchestration functions that coordinate video and audio processing. +For direct access to video/audio processors, see video_processor.py and audio_processor.py. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from typing import Any, Dict, List, Optional, Tuple + +import numpy as np +import torch +from PIL import Image + +# Import from split modules +from sonic_o1_agent.core.audio_processor import ( + load_audio_pyav, + process_audio_with_metadata, +) +from sonic_o1_agent.core.multimodal_utils import ( + SAMPLE_RATE, + ceil_by_factor, + floor_by_factor, + get_index, + round_by_factor, + smart_nframes, + smart_resize, +) +from sonic_o1_agent.core.video_processor import ( + VideoMetadata, + fetch_video_pyav, + get_video_metadata, + process_video_with_metadata, +) + +# Re-export for backward compatibility +__all__ = [ + # Constants + "SAMPLE_RATE", + # Math helpers + "round_by_factor", + "ceil_by_factor", + "floor_by_factor", + "smart_resize", + "smart_nframes", + "get_index", + # Video functions + "VideoMetadata", + "get_video_metadata", + "fetch_video_pyav", + "process_video_with_metadata", + # Audio functions + "load_audio_pyav", + "process_audio_with_metadata", + # Orchestration functions + "process_audio_info_pyav", + "process_vision_info_pyav", + "process_mm_info", +] + + +def process_audio_info_pyav( + conversations, + use_audio_in_video: bool, + max_audio_duration: Optional[float] = None, + max_audio_chunks: Optional[int] = None, + audio_chunk_duration_sec: float = 10.0, +) -> Optional[List[np.ndarray]]: + """Process audio from conversation structure.""" + audios = [] + + if isinstance(conversations[0], dict): + conversations = [conversations] + + for conversation in conversations: + for message in conversation: + if not isinstance(message["content"], list): + continue + + for ele in message["content"]: + if ele["type"] != "audio": + continue + + if "audio" not in ele and "audio_url" not in ele: + continue + + path = ele.get("audio", ele.get("audio_url")) + audio_start: float = ele.get("audio_start", 0.0) + audio_end: Optional[float] = ele.get("audio_end", None) + + if isinstance(path, np.ndarray): + if path.ndim > 1: + raise ValueError("Only mono audio supported") + + start_idx = int(SAMPLE_RATE * audio_start) + end_idx = ( + None if audio_end is None else int(SAMPLE_RATE * audio_end) + ) + audio = path[start_idx:end_idx] + + if max_audio_chunks is not None: + samples_per_chunk = int(audio_chunk_duration_sec * SAMPLE_RATE) + total_samples = len(audio) + num_chunks = int(np.ceil(total_samples / samples_per_chunk)) + + if num_chunks > max_audio_chunks: + chunks = [] + for i in range(num_chunks): + start_idx = i * samples_per_chunk + end_idx = min( + (i + 1) * samples_per_chunk, total_samples + ) + chunks.append(audio[start_idx:end_idx]) + + # Evenly sample chunks across the timeline (beginning, middle, end) + # rather than taking only the first max_audio_chunks. + sample_indices = np.linspace( + 0, num_chunks - 1, max_audio_chunks, dtype=int + ) + sampled_chunks = [chunks[i] for i in sample_indices] + audio = np.concatenate(sampled_chunks, axis=0) + elif max_audio_duration is not None: + max_samples = int(SAMPLE_RATE * max_audio_duration) + if len(audio) > max_samples: + audio = audio[:max_samples] + + audios.append(audio) + continue + + if path.startswith("file://"): + path = path[7:] + + duration: Optional[float] = ( + None if audio_end is None else (audio_end - audio_start) + ) + + audio = load_audio_pyav( + path, + sr=SAMPLE_RATE, + offset=audio_start, + duration=duration, + max_duration=max_audio_duration, + max_chunks=max_audio_chunks, + chunk_duration_sec=audio_chunk_duration_sec, + ) + audios.append(audio) + + return audios if audios else None + + +def process_vision_info_pyav( + conversations, +) -> Tuple[Optional[List[Image.Image]], Optional[List[torch.Tensor]]]: + """Process images and videos from conversation structure.""" + videos = [] + images = [] + + if isinstance(conversations[0], dict): + conversations = [conversations] + + for conversation in conversations: + for message in conversation: + if not isinstance(message["content"], list): + continue + + for ele in message["content"]: + if ele["type"] == "video" and ("video" in ele or "video_url" in ele): + videos.append(fetch_video_pyav(ele)) + elif ele["type"] == "image" and "image" in ele: + img_path = ele["image"] + if img_path.startswith("file://"): + img_path = img_path[7:] + images.append(Image.open(img_path).convert("RGB")) + + return (images if images else None, videos if videos else None) + + +def process_mm_info( + conversations, + use_audio_in_video: bool = False, + max_audio_duration: Optional[float] = None, + max_audio_chunks: Optional[int] = None, + audio_chunk_duration_sec: float = 10.0, +) -> tuple: + """Process multimodal info from conversations. + + Returns: + Tuple of (audios, images, videos, metadata) + metadata includes frame indices, timestamps, etc. + """ + audios: List[Any] = [] + images: List[Any] = [] + videos: List[Any] = [] + # Metadata per item: video_metadata has duration_sec, fps, total_frames, + # frames_sampled, sampling_interval_sec, coverage_sec; audio_metadata has + # duration_sec, sample_rate, chunks_analyzed, chunk_duration_sec, coverage_sec. + metadata: Dict[str, List[Any]] = { + "video_metadata": [], + "audio_metadata": [], + } + + # Process conversation content FIRST + for message in conversations: + if isinstance(message, dict) and "content" in message: + for item in message["content"]: + if isinstance(item, dict): + if item["type"] == "video": + video_path = item["video"] + + # Process video and GET metadata + video_frames, video_meta = process_video_with_metadata( + video_path, + max_frames=item.get("max_frames", 256), + min_frames=item.get("min_frames", 64), + video_start=item.get("video_start"), + video_end=item.get("video_end"), + ) + + videos.append(video_frames) + metadata["video_metadata"].append(video_meta) + + elif item["type"] == "audio": + audio_path = item["audio"] + + # Process audio and GET metadata + audio_chunks, audio_meta = process_audio_with_metadata( + audio_path, + max_audio_duration, + max_audio_chunks, + audio_chunk_duration_sec, + audio_start=item.get("audio_start"), + audio_end=item.get("audio_end"), + ) + + audios.append(audio_chunks) + metadata["audio_metadata"].append(audio_meta) + + elif item["type"] == "image": + # Handle images if needed + pass + + return audios, images, videos, metadata diff --git a/src/sonic_o1_agent/core/multimodal_utils.py b/src/sonic_o1_agent/core/multimodal_utils.py new file mode 100644 index 0000000..bd32250 --- /dev/null +++ b/src/sonic_o1_agent/core/multimodal_utils.py @@ -0,0 +1,116 @@ +"""Shared utilities for multimodal processing. + +Math helpers, constants, and common utilities used by video and audio processors. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import math +import os +from functools import lru_cache +from typing import List, Tuple + +# ============================================================================ +# Constants +# ============================================================================ +IMAGE_FACTOR = 28 +MIN_PIXELS = 4 * 28 * 28 +MAX_PIXELS = 16384 * 28 * 28 +VIDEO_MIN_PIXELS = 128 * 28 * 28 +VIDEO_MAX_PIXELS = 768 * 28 * 28 +VIDEO_TOTAL_PIXELS = int( + float(os.environ.get("VIDEO_MAX_PIXELS", 128000 * 28 * 28 * 0.9)) +) +FRAME_FACTOR = 2 +FPS = 1.0 +FPS_MIN_FRAMES = 4 +FPS_MAX_FRAMES = 768 +SAMPLE_RATE = 16000 + + +# ============================================================================ +# Optimized Math Helpers +# ============================================================================ +@lru_cache(maxsize=1024) +def round_by_factor(number: int, factor: int) -> int: + """Return the closest integer to 'number' that is divisible by 'factor'.""" + return round(number / factor) * factor + + +@lru_cache(maxsize=1024) +def ceil_by_factor(number: int, factor: int) -> int: + """Return the smallest integer >= 'number' that is divisible by 'factor'.""" + return math.ceil(number / factor) * factor + + +@lru_cache(maxsize=1024) +def floor_by_factor(number: int, factor: int) -> int: + """Return the largest integer <= 'number' that is divisible by 'factor'.""" + return math.floor(number / factor) * factor + + +@lru_cache(maxsize=2048) +def smart_resize( + height: int, + width: int, + factor: int = IMAGE_FACTOR, + min_pixels: int = MIN_PIXELS, + max_pixels: int = MAX_PIXELS, +) -> Tuple[int, int]: + """Calculate optimal resize dimensions maintaining aspect ratio.""" + h_bar = max(factor, round_by_factor(height, factor)) + w_bar = max(factor, round_by_factor(width, factor)) + + if h_bar * w_bar > max_pixels: + beta = math.sqrt((height * width) / max_pixels) + h_bar = max(factor, floor_by_factor(int(height / beta), factor)) + w_bar = max(factor, floor_by_factor(int(width / beta), factor)) + elif h_bar * w_bar < min_pixels: + beta = math.sqrt(min_pixels / (height * width)) + h_bar = ceil_by_factor(int(height * beta), factor) + w_bar = ceil_by_factor(int(width * beta), factor) + + return h_bar, w_bar + + +def smart_nframes(ele: dict, total_frames: int, video_fps: float) -> int: + """Calculate optimal number of frames to sample from video.""" + if "nframes" in ele: + return round_by_factor(ele["nframes"], FRAME_FACTOR) + + fps = ele.get("fps", FPS) + min_frames = ceil_by_factor(ele.get("min_frames", FPS_MIN_FRAMES), FRAME_FACTOR) + max_frames = floor_by_factor( + ele.get("max_frames", min(FPS_MAX_FRAMES, total_frames)), FRAME_FACTOR + ) + + nframes = int(total_frames * fps / video_fps) + nframes = max(min_frames, min(nframes, max_frames, total_frames)) + nframes = floor_by_factor(nframes, FRAME_FACTOR) + + if not (FRAME_FACTOR <= nframes <= total_frames): + raise ValueError( + f"nframes should be in [{FRAME_FACTOR}, {total_frames}], got {nframes}" + ) + + return nframes + + +def get_index(total_frames: int, num_frames: int) -> List[int]: + """Calculate frame indices to sample uniformly. + + Args: + total_frames: Total number of frames in video + num_frames: Number of frames to sample + + Returns: + List of frame indices to sample + """ + import numpy as np + + if num_frames >= total_frames: + return list(range(total_frames)) + + # Uniform sampling + indices = np.linspace(0, total_frames - 1, num_frames, dtype=np.int32) + return [int(x) for x in indices] diff --git a/src/sonic_o1_agent/core/video_processor.py b/src/sonic_o1_agent/core/video_processor.py new file mode 100644 index 0000000..b51a2dc --- /dev/null +++ b/src/sonic_o1_agent/core/video_processor.py @@ -0,0 +1,316 @@ +"""Video processing using PyAV. + +Memory-efficient video frame sampling and processing. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +import time +from dataclasses import dataclass +from typing import Dict, Optional, cast + +import av +import numpy as np +import torch +from PIL import Image +from torchvision import transforms +from torchvision.transforms import InterpolationMode + +from sonic_o1_agent.core.multimodal_utils import ( + FRAME_FACTOR, + IMAGE_FACTOR, + VIDEO_MAX_PIXELS, + VIDEO_MIN_PIXELS, + VIDEO_TOTAL_PIXELS, + get_index, + smart_nframes, + smart_resize, +) + +logger = logging.getLogger(__name__) + + +# ============================================================================ +# Video Metadata +# ============================================================================ +@dataclass +class VideoMetadata: + """Cached video metadata to avoid repeated file opens.""" + + total_frames: int + fps: float + width: int + height: int + + +_video_metadata_cache: Dict[str, VideoMetadata] = {} + + +def get_video_metadata(video_path: str) -> VideoMetadata: + """Extract and cache video metadata.""" + if video_path in _video_metadata_cache: + return _video_metadata_cache[video_path] + + container = av.open(video_path) + video_stream = container.streams.video[0] + + metadata = VideoMetadata( + total_frames=video_stream.frames or sum(1 for _ in container.decode(video=0)), + fps=float(video_stream.average_rate), + width=video_stream.width, + height=video_stream.height, + ) + + container.close() + _video_metadata_cache[video_path] = metadata + return metadata + + +# ============================================================================ +# Video Processing Functions +# ============================================================================ +def fetch_video_pyav(ele: dict, image_factor: int = IMAGE_FACTOR) -> torch.Tensor: + """Memory-efficient video loading using PyAV.""" + video_path = ele["video"] + st = time.time() + + try: + metadata = get_video_metadata(video_path) + total_frames = metadata.total_frames + video_fps = metadata.fps + height = metadata.height + width = metadata.width + except Exception: + container = av.open(video_path) + video_stream = container.streams.video[0] + total_frames = video_stream.frames or sum(1 for _ in container.decode(video=0)) + video_fps = float(video_stream.average_rate) + height = video_stream.height + width = video_stream.width + container.close() + + video_start = ele.get("video_start", 0.0) + video_end = ele.get("video_end") + + start_frame = max(0, int(video_start * video_fps)) if video_start > 0 else 0 + if video_end is not None: + end_frame = min(int(video_end * video_fps), total_frames - 1) + total_frames = end_frame - start_frame + 1 + else: + end_frame = total_frames - 1 + + nframes = smart_nframes(ele, total_frames=total_frames, video_fps=video_fps) + + if nframes == total_frames: + indices = list(range(start_frame, end_frame + 1)) + else: + indices = np.linspace(start_frame, end_frame, nframes, dtype=np.int32).tolist() + + indices_set = set(indices) + + container = av.open( + video_path, + options={ + "threads": "auto", + "thread_queue_size": "512", + }, + ) + video_stream = container.streams.video[0] + video_stream.thread_type = "AUTO" + + min_pixels = ele.get("min_pixels", VIDEO_MIN_PIXELS) + total_pixels = ele.get("total_pixels", VIDEO_TOTAL_PIXELS) + max_pixels = max( + min(VIDEO_MAX_PIXELS, total_pixels / nframes * FRAME_FACTOR), + int(min_pixels * 1.05), + ) + max_pixels = min(ele.get("max_pixels", max_pixels), max_pixels) + + if "resized_height" in ele and "resized_width" in ele: + resized_height, resized_width = smart_resize( + ele["resized_height"], ele["resized_width"], factor=image_factor + ) + else: + resized_height, resized_width = smart_resize( + height, + width, + factor=image_factor, + min_pixels=min_pixels, + max_pixels=max_pixels, + ) + + frames_array = np.empty((nframes, resized_height, resized_width, 3), dtype=np.uint8) + + if start_frame > 10: + try: + container.seek( + int(start_frame / video_fps * av.time_base), stream=video_stream + ) + frame_idx = start_frame + except Exception: + frame_idx = 0 + else: + frame_idx = 0 + + collected = 0 + resize_transform = transforms.Resize( + (resized_height, resized_width), + interpolation=InterpolationMode.BICUBIC, + antialias=True, + ) + + for frame in container.decode(video=0): + if frame_idx in indices_set: + frame_np = frame.to_ndarray(format="rgb24") + frame_tensor = torch.from_numpy(frame_np).permute(2, 0, 1) + frame_resized = resize_transform(frame_tensor) + frames_array[collected] = frame_resized.permute(1, 2, 0).numpy() + + collected += 1 + if collected >= nframes: + break + + frame_idx += 1 + if frame_idx > end_frame: + break + + container.close() + + video = torch.from_numpy(frames_array).permute(0, 3, 1, 2).float() + + logger.info( + f" Video: {nframes} frames ({height}x{width} -> {resized_height}x{resized_width}) in {time.time() - st:.3f}s" + ) + return video + + +def process_video_with_metadata( + video_path: str, + max_frames: int = 256, + min_frames: int = 64, + video_start: Optional[float] = None, + video_end: Optional[float] = None, +) -> tuple: + """Process video and return frames + compact metadata summary. + + Supports optional ``video_start`` / ``video_end`` (seconds) to + restrict processing to a time range within the video. When omitted + the full video is processed. + + Metadata includes only aggregate stats (duration, frame count, + sampling interval, coverage range) -- no per-frame indices or + timestamps, keeping the output concise and actionable. + """ + container = av.open(video_path) + video_stream = container.streams.video[0] + + # Get video properties + fps = float(video_stream.average_rate) + total_frames_full = video_stream.frames + if total_frames_full == 0: + full_duration = float(container.duration / av.time_base) + total_frames_full = int(full_duration * fps) + else: + full_duration = total_frames_full / fps + + # Resolve time-range to frame range + start_frame = 0 + end_frame = total_frames_full - 1 + if video_start is not None and video_start > 0: + start_frame = max(0, int(video_start * fps)) + if video_end is not None: + end_frame = min(int(video_end * fps), total_frames_full - 1) + + range_frames = max(end_frame - start_frame + 1, 1) + duration_sec = range_frames / fps + + # Calculate number of frames to sample within the range + num_frames = min(max_frames, range_frames) + num_frames = max(min_frames, num_frames) + + # Calculate indices to sample (relative to start_frame) + raw_indices = get_index(range_frames, num_frames) + indices_set = set(idx + start_frame for idx in raw_indices) + + sampled_count = 0 + first_frame_sec = None + last_frame_sec = None + frames = [] + + # Seek if the range doesn't start at the beginning. + # After seek, set frame_idx to start_frame so the manual counter + # tracks the real position (enumerate resets to 0 after seek). + if start_frame > 10: + try: + container.seek( + int(start_frame / fps * av.time_base), + stream=video_stream, + ) + frame_idx = start_frame + except Exception: + frame_idx = 0 # fall back to sequential decode + else: + frame_idx = 0 + + for frame in container.decode(video=0): + if frame_idx > end_frame: + break + if frame_idx in indices_set: + timestamp_sec = frame_idx / fps + + if first_frame_sec is None: + first_frame_sec = timestamp_sec + last_frame_sec = timestamp_sec + + sampled_count += 1 + + # Process frame + img = frame.to_ndarray(format="rgb24") + h, w = img.shape[:2] + + resized_h, resized_w = smart_resize(h, w) + + pil_img = Image.fromarray(img) + pil_img = pil_img.resize((resized_w, resized_h), Image.BICUBIC) + img_resized = np.array(pil_img) + + frames.append(img_resized) + + if len(frames) >= num_frames: + break + + frame_idx += 1 + + container.close() + + # Compact coverage summary -- seconds only + sampling_interval = ( + round( + (cast(float, last_frame_sec) - cast(float, first_frame_sec)) + / max(sampled_count - 1, 1), + 2, + ) + if sampled_count > 1 + else 0.0 + ) + + metadata = { + "duration_sec": round(duration_sec, 2), + "fps": round(fps, 2), + "total_frames": range_frames, + "frames_sampled": sampled_count, + "sampling_interval_sec": sampling_interval, + "coverage_sec": [ + round(first_frame_sec or 0.0, 2), + round(last_frame_sec or 0.0, 2), + ], + } + + # Convert frames to tensor + if frames: + frames_tensor = torch.from_numpy(np.array(frames)).permute(0, 3, 1, 2).float() + else: + frames_tensor = torch.empty(0) + + return frames_tensor, metadata diff --git a/src/sonic_o1_agent/demo/static/Vector-Logo-FullColour_Horizontal.png b/src/sonic_o1_agent/demo/static/Vector-Logo-FullColour_Horizontal.png new file mode 100644 index 0000000000000000000000000000000000000000..0106314aee8e1ade1d5d4569fe9b868cca6e74a6 GIT binary patch literal 18330 zcmeIaRa9I-*EZNB0TKcv5D3<|6FdaBU=0LoBtUR?4cd5cYX}+$-ob)9!3pj(uEE_k zICIGR&a7E$*8DRUb1@g+>5Egc_pWC@C8ti+sSZ_AfM8(|V}L*)EE#DDWe^Ah4gx)1 zeEJwj;Yi+n2HawmJ zDe3F$+uPgw`uavkM`vVYl$MsZw6qKl4=*k*9v&X1_06w>KvE$x67N;rW_DZs05c%p zjN5#I{6`PL|GEBG44A&ae)5p-d!tA4UhRLb{|5#FQw6&92{9gl_$dR5g=q_v$}tS~ zeHs#&il!A`dmT``_+N4T|0?go&Isthwe3zKj#$9Gr6D!R9jKwodZA+*;C@OB@!mxN zfg;z>`q@5!K%$8zeR^8JJ*v!X3HAuIm`&^QQ(F-Pl2F`L{gnm+g(OVnI|`wIh7G`) z|KjGMsZ5ivfFUA*<;}yLv@y68U+)MQig<^VNdI8t^CFz$P1~+!$IqFcZ 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a/src/sonic_o1_agent/demo/static/script.js b/src/sonic_o1_agent/demo/static/script.js new file mode 100644 index 0000000..eb52388 --- /dev/null +++ b/src/sonic_o1_agent/demo/static/script.js @@ -0,0 +1,723 @@ +/* ============================================================ + Sonic O1 Agent β€” Frontend Logic + Handles: video upload, SSE streaming, pipeline progress, + result rendering (answer, plan, reasoning, reflection) + ============================================================ */ + + const uploadZone = document.getElementById("upload-zone"); + const videoInput = document.getElementById("video-input"); + const filePreview = document.getElementById("file-preview"); + const fileName = document.getElementById("file-name"); + const fileSize = document.getElementById("file-size"); + const fileRemove = document.getElementById("file-remove"); + const queryText = document.getElementById("query-text"); + const charCountEl = document.getElementById("char-count"); + const analyzeBtn = document.getElementById("analyze-btn"); + const pipelineEl = document.getElementById("pipeline"); + const resultsEl = document.getElementById("results"); + + const MAX_CHARS = 2000; + let selectedFile = null; + let currentAbort = null; + + // ============================================================ + // FILE UPLOAD + // ============================================================ + + uploadZone.addEventListener("click", () => videoInput.click()); + uploadZone.addEventListener("dragover", (e) => { + e.preventDefault(); + uploadZone.classList.add("dragover"); + }); + uploadZone.addEventListener("dragleave", () => { + uploadZone.classList.remove("dragover"); + }); + uploadZone.addEventListener("drop", (e) => { + e.preventDefault(); + uploadZone.classList.remove("dragover"); + if (e.dataTransfer.files.length > 0) { + handleFile(e.dataTransfer.files[0]); + } + }); + + videoInput.addEventListener("change", () => { + if (videoInput.files.length > 0) handleFile(videoInput.files[0]); + }); + + fileRemove.addEventListener("click", () => { + selectedFile = null; + videoInput.value = ""; + filePreview.classList.add("hidden"); + uploadZone.classList.remove("hidden"); + updateAnalyzeBtn(); + }); + + function handleFile(file) { + if (!file.type.startsWith("video/")) { + alert("Please select a video file."); + return; + } + selectedFile = file; + fileName.textContent = file.name; + fileSize.textContent = formatSize(file.size); + filePreview.classList.remove("hidden"); + uploadZone.classList.add("hidden"); + updateAnalyzeBtn(); + } + + function formatSize(bytes) { + if (bytes < 1024 * 1024) return (bytes / 1024).toFixed(0) + " KB"; + return (bytes / (1024 * 1024)).toFixed(1) + " MB"; + } + + // ============================================================ + // EXAMPLE CHIPS + // ============================================================ + + document.querySelectorAll(".example-chip").forEach(chip => { + chip.addEventListener("click", () => { + queryText.value = chip.dataset.text; + queryText.dispatchEvent(new Event("input")); + queryText.focus(); + }); + }); + + // ============================================================ + // CHAR COUNTER + BUTTON STATE + // ============================================================ + + queryText.addEventListener("input", () => { + const len = queryText.value.length; + charCountEl.textContent = `${len} / ${MAX_CHARS}`; + charCountEl.className = "char-count"; + if (len > MAX_CHARS * 0.9) charCountEl.classList.add("warn"); + if (len >= MAX_CHARS) charCountEl.classList.add("error"); + updateAnalyzeBtn(); + }); + + function updateAnalyzeBtn() { + analyzeBtn.disabled = !(selectedFile && queryText.value.trim().length > 0); + } + + // ============================================================ + // ANALYZE β€” SSE STREAM + // ============================================================ + + analyzeBtn.addEventListener("click", runAnalysis); + queryText.addEventListener("keydown", (e) => { + if ((e.metaKey || e.ctrlKey) && e.key === "Enter") runAnalysis(); + }); + + // Map LangGraph node names to our pipeline UI nodes + const NODE_MAP = { + "planning": "planning", + "segmentation": "segmentation", + "temporal_indexing": "temporal_indexing", + "multi_step": "inference", + "reasoning": "inference", + "direct": "inference", + "reflection": "reflection", + "cleanup": "cleanup", + }; + + async function runAnalysis() { + const query = queryText.value.trim(); + if (!selectedFile || !query) return; + if (query.length > MAX_CHARS) { + alert("Query too long."); + return; + } + + // Reset UI + analyzeBtn.disabled = true; + resultsEl.classList.add("hidden"); + pipelineEl.classList.remove("hidden"); + resetPipeline(); + + // Cancel any in-flight request + if (currentAbort) currentAbort.abort(); + currentAbort = new AbortController(); + + // Build form data + const formData = new FormData(); + formData.append("video", selectedFile); + formData.append("query", query); + + const startTime = Date.now(); + let completedNodes = new Set(); + + // Live elapsed timer on active node + const timerInterval = setInterval(() => { + const activeNode = document.querySelector(".pipeline-node.active"); + if (activeNode) { + const elapsed = ((Date.now() - startTime) / 1000).toFixed(0); + activeNode.querySelector(".node-time").textContent = elapsed + "s..."; + } + }, 1000); + + try { + const res = await fetch("/analyze/stream", { + method: "POST", + body: formData, + signal: currentAbort.signal, + }); + + if (!res.ok) { + let detail = "Server error (" + res.status + ")"; + try { detail = (await res.json()).detail || detail; } catch {} + throw new Error(detail); + } + + const reader = res.body.getReader(); + const decoder = new TextDecoder(); + let buffer = ""; + + while (true) { + const { done, value } = await reader.read(); + if (done) break; + + buffer += decoder.decode(value, { stream: true }); + const lines = buffer.split("\n"); + buffer = lines.pop(); + + for (const line of lines) { + if (!line.startsWith("data: ")) continue; + let payload; + try { payload = JSON.parse(line.slice(6)); } catch { continue; } + + if (payload.node !== undefined) { + // Pipeline progress event + const uiNode = NODE_MAP[payload.node] || payload.node; + markNodeDone(uiNode, payload.elapsed); + completedNodes.add(uiNode); + + // ── PLANNING ────────────────────────────────────── + if (payload.node === "planning") { + if (payload.query_type) { + addThinking("planning", `Query type: ${escapeHtml(payload.query_type)}`); + } + if (payload.plan) { + const p = payload.plan; + if (p.duration_seconds) addThinking("planning", `Video duration: ${Math.round(p.duration_seconds)}s | Max frames: ${p.max_frames}`); + } + if (payload.multi_step_plan) { + updateNodeDesc("planning", `${payload.multi_step_plan.length} steps planned`); + addThinking("planning", `Decomposed into ${payload.multi_step_plan.length} steps:`); + payload.multi_step_plan.forEach((step, i) => { + const desc = step.description || step.action || JSON.stringify(step); + addThinking("planning", `${step.step_id || i + 1}. ${escapeHtml(desc)}`); + }); + } else { + addThinking("planning", "Direct inference mode β€” no decomposition needed"); + } + } + + // ── SEGMENTATION ────────────────────────────────── + if (payload.node === "segmentation") { + addThinking("processing", "Video preprocessed for analysis"); + } + + // ── TEMPORAL INDEXING ────────────────────────────── + if (payload.node === "temporal_indexing") { + if (payload.temporal_index) { + updateNodeDesc("temporal_indexing", `${payload.temporal_segments || "?"} segments indexed`); + addThinking("processing", `Temporal Index β€” ${payload.temporal_segments || "?"} segments captioned:`); + // Show each segment caption + const lines = payload.temporal_index.split("\n").filter(l => l.trim()); + lines.forEach(line => { + addThinking("processing", escapeHtml(line)); + }); + } else { + addThinking("processing", "Temporal index unavailable."); + } + } + + // ── INFERENCE (direct / reasoning / multi_step) ─── + if (payload.node === "direct" || payload.node === "reasoning" || payload.node === "multi_step") { + updateNodeDesc("inference", "Response generated"); + + if (payload.reasoning_chain && payload.reasoning_chain.length > 0) { + addThinking("inference", `Chain-of-Thought β€” ${payload.reasoning_chain.length} reasoning steps:`); + payload.reasoning_chain.forEach(step => { + const action = step.action || ""; + const result = step.result || step.thought || ""; + addThinking("inference", `Step ${step.step || "?"}: ${escapeHtml(action)}`); + if (result) addThinking("inference", escapeHtml(result)); + }); + } + + if (payload.steps_executed) { + addThinking("inference", `Multi-step execution: ${Object.keys(payload.steps_executed).length} steps completed`); + } + + if (payload.response) { + addThinking("inference", `Generated Response:`); + addThinkingBlock("inference", payload.response); + } + + if (payload.evidence) { + if (payload.evidence.video) { + const v = payload.evidence.video; + addThinking("inference", `Evidence: ${v.frames_analyzed} frames analyzed, ${v.coverage_sec?.toFixed(0) || "?"}s coverage`); + } + if (payload.evidence.audio) { + const a = payload.evidence.audio; + addThinking("inference", `Evidence: ${a.chunks_analyzed} audio chunks, ${a.coverage_sec?.toFixed(0) || "?"}s coverage`); + } + } + } + + // ── REFLECTION ──────────────────────────────────── + if (payload.node === "reflection") { + const ref = payload.reflection || {}; + const conf = ref.final_confidence || ref.confidence; + if (conf) { + updateNodeDesc("reflection", `Confidence: ${(conf * 100).toFixed(0)}%`); + addThinking("reflection", `Self-evaluation confidence: ${(conf * 100).toFixed(0)}%`); + } + if (ref.scores) { + const s = ref.scores; + const parts = []; + if (s.completeness != null) parts.push(`Completeness: ${s.completeness}/10`); + if (s.accuracy != null) parts.push(`Accuracy: ${s.accuracy}/10`); + if (s.clarity != null) parts.push(`Clarity: ${s.clarity}/10`); + if (s.evidence != null) parts.push(`Evidence: ${s.evidence}/10`); + if (parts.length) addThinking("reflection", parts.join(" Β· ")); + } + if (ref.strengths && ref.strengths.length) { + const clean = ref.strengths.filter(s => s && s.replace(/\*/g, "").trim().length > 0); + if (clean.length) addThinking("reflection", `Strengths: ${clean.map(s => escapeHtml(s)).join("; ")}`); + } + if (ref.weaknesses && ref.weaknesses.length) { + const clean = ref.weaknesses.filter(w => w && w.replace(/\*/g, "").trim().length > 0); + if (clean.length) addThinking("reflection", `Weaknesses: ${clean.map(w => escapeHtml(w)).join("; ")}`); + } + if (ref.total_attempts && ref.total_attempts > 1) { + addThinking("reflection", `Refined over ${ref.total_attempts} attempts`); + } + if (payload.hallucination_assessment) { + const h = payload.hallucination_assessment; + if (h.has_hallucination) { + addThinking("reflection", `⚠ Hallucination detected (severity: ${h.severity})`); + } else { + addThinking("reflection", `βœ“ No hallucinations detected`); + } + } + if (payload.was_refined) { + addThinking("reflection", `Response was refined based on self-evaluation`); + } + } + + // ── CLEANUP ─────────────────────────────────────── + if (payload.node === "cleanup") { + addThinking("processing", "βœ“ Pipeline complete β€” rendering final results"); + } + + // Activate next node + const allNodes = ["planning", "segmentation", "temporal_indexing", "inference", "reflection", "cleanup"]; + const idx = allNodes.indexOf(uiNode); + if (idx >= 0 && idx < allNodes.length - 1) { + activateNode(allNodes[idx + 1]); + } + + } else if (payload.result !== undefined) { + // Final result + clearInterval(timerInterval); + renderResults(payload.result); + + } else if (payload.error !== undefined) { + clearInterval(timerInterval); + throw new Error(payload.error); + } + } + } + + } catch (err) { + clearInterval(timerInterval); + alert("Analysis failed: " + err.message); + } + + analyzeBtn.disabled = false; + } + + // ============================================================ + // PIPELINE UI + // ============================================================ + + function updateNodeDesc(nodeName, text) { + const el = document.querySelector(`.pipeline-node[data-node="${nodeName}"] .node-desc`); + if (el) el.textContent = text; + } + + function addThinking(type, html) { + const log = document.getElementById("thinking-log"); + if (!log) return; + const entry = document.createElement("div"); + entry.className = "thinking-entry"; + entry.innerHTML = ` + ${type} + ${html} + `; + log.appendChild(entry); + log.scrollTop = log.scrollHeight; + } + + function addThinkingBlock(type, text) { + const log = document.getElementById("thinking-log"); + if (!log) return; + const entry = document.createElement("div"); + entry.className = "thinking-entry"; + entry.innerHTML = ` + ${type} +
${renderMarkdown(text)}
+ `; + log.appendChild(entry); + log.scrollTop = log.scrollHeight; + } + + function resetPipeline() { + document.querySelectorAll(".pipeline-node").forEach(n => { + n.classList.remove("active", "done"); + n.querySelector(".node-time").textContent = ""; + }); + document.querySelectorAll(".pipeline-connector").forEach(c => { + c.classList.remove("done"); + }); + // Clear thinking log + const log = document.getElementById("thinking-log"); + if (log) log.innerHTML = ""; + // Reset node descriptions + updateNodeDesc("planning", "Decomposing query into steps"); + updateNodeDesc("segmentation", "Splitting video into segments"); + updateNodeDesc("temporal_indexing", "Captioning segments for grounding"); + updateNodeDesc("inference", "Multi-step reasoning & analysis"); + updateNodeDesc("reflection", "Self-evaluation & refinement"); + updateNodeDesc("cleanup", "Finalising results"); + // Activate first node + activateNode("planning"); + } + + function activateNode(nodeName) { + const el = document.querySelector(`.pipeline-node[data-node="${nodeName}"]`); + if (el && !el.classList.contains("done")) { + el.classList.add("active"); + } + } + + function markNodeDone(nodeName, elapsed) { + const el = document.querySelector(`.pipeline-node[data-node="${nodeName}"]`); + if (!el) return; + el.classList.remove("active"); + el.classList.add("done"); + if (elapsed != null) { + el.querySelector(".node-time").textContent = elapsed.toFixed(1) + "s"; + } + + // Mark connector before this node as done + const prev = el.previousElementSibling; + if (prev && prev.classList.contains("pipeline-connector")) { + prev.classList.add("done"); + } + } + + // ============================================================ + // RENDER RESULTS + // ============================================================ + + function renderResults(data) { + resultsEl.classList.remove("hidden"); + resultsEl.scrollIntoView({ behavior: "smooth", block: "start" }); + + // Answer β€” render with markdown formatting + document.getElementById("answer-text").innerHTML = renderMarkdown(data.response || ""); + + // Confidence + renderConfidence(data); + + // Multi-step plan + renderPlan(data); + + // Reasoning chain + renderReasoning(data); + + // Reflection + renderReflection(data); + } + + function renderConfidence(data) { + const badge = document.getElementById("confidence-value"); + let confidence = null; + + if (data.reflection && data.reflection.final_confidence != null) { + confidence = data.reflection.final_confidence; + } else if (data.reflection && data.reflection.confidence != null) { + confidence = data.reflection.confidence; + } else if (data.confidence != null) { + confidence = data.confidence; + } + + if (confidence != null) { + const pct = (confidence * 100).toFixed(0) + "%"; + badge.textContent = pct; + badge.className = "confidence-value"; + if (confidence >= 0.8) badge.classList.add("high"); + else if (confidence >= 0.5) badge.classList.add("medium"); + else badge.classList.add("low"); + } else { + badge.textContent = "β€”"; + badge.className = "confidence-value"; + } + } + + function renderPlan(data) { + const section = document.getElementById("plan-section"); + const list = document.getElementById("plan-list"); + list.innerHTML = ""; + + const plan = data.multi_step_plan || []; + if (plan.length === 0) { + section.classList.add("hidden"); + return; + } + + section.classList.remove("hidden"); + + plan.forEach((step, idx) => { + const el = document.createElement("div"); + el.className = "plan-step"; + el.innerHTML = ` + Step ${step.step_id || idx + 1} + ${escapeHtml(step.description || step.action || JSON.stringify(step))} + `; + list.appendChild(el); + }); + } + + function renderReasoning(data) { + const section = document.getElementById("reasoning-section"); + const list = document.getElementById("reasoning-list"); + list.innerHTML = ""; + + const chain = data.reasoning_chain || []; + if (chain.length === 0) { + section.classList.add("hidden"); + return; + } + + section.classList.remove("hidden"); + + chain.forEach((step) => { + const el = document.createElement("div"); + el.className = "reasoning-step"; + el.innerHTML = ` +
+ Step ${step.step || "?"} + ${escapeHtml(step.action || "")} +
+
${renderMarkdown(step.result || step.thought || "")}
+ `; + list.appendChild(el); + }); + } + + function renderReflection(data) { + const section = document.getElementById("reflection-section"); + const ref = data.reflection; + + if (!ref) { + section.classList.add("hidden"); + return; + } + + section.classList.remove("hidden"); + + // Score cards β€” only available in single-shot reflection mode + const scoresEl = document.getElementById("score-cards"); + scoresEl.innerHTML = ""; + + const scores = ref.scores || {}; + const criteria = ["completeness", "accuracy", "clarity", "evidence"]; + let hasScores = false; + + criteria.forEach(key => { + const val = scores[key]; + if (val == null) return; + hasScores = true; + const pct = val * 10; + const tier = pct >= 80 ? "high" : pct >= 50 ? "medium" : "low"; + + const card = document.createElement("div"); + card.className = "score-card"; + card.innerHTML = ` + ${key} + ${val}/10 +
+ `; + scoresEl.appendChild(card); + }); + + // In iterative mode, show total attempts as a summary card if no scores + if (!hasScores && ref.total_attempts) { + const card = document.createElement("div"); + card.className = "score-card"; + card.innerHTML = ` + Refinement Rounds + ${ref.total_attempts} +
+ `; + scoresEl.appendChild(card); + } + + // Hallucination + const hallCard = document.getElementById("hallucination-card"); + const hallValue = document.getElementById("hallucination-value"); + const hall = data.hallucination_assessment; + + if (hall) { + hallCard.classList.remove("hidden"); + const sev = (hall.severity || "NONE").toLowerCase(); + hallValue.textContent = hall.has_hallucination ? sev.toUpperCase() : "None detected"; + hallValue.className = "hallucination-value " + (hall.has_hallucination ? sev : "none"); + } else { + hallCard.classList.add("hidden"); + } + + // Refinement history + const refSection = document.getElementById("refinement-section"); + const refList = document.getElementById("refinement-list"); + refList.innerHTML = ""; + + const history = data.refinement_history || (ref.refinement_history) || []; + if (history.length > 0) { + refSection.classList.remove("hidden"); + history.forEach(item => { + const el = document.createElement("div"); + el.className = "refinement-item"; + const recClass = (item.recommendation || "").toLowerCase(); + el.innerHTML = ` + Attempt ${item.attempt} + ${(item.confidence * 100).toFixed(0)}% + ${item.recommendation || ""} + `; + refList.appendChild(el); + }); + } else { + refSection.classList.add("hidden"); + } + } + + // ============================================================ + // COPY BUTTON + // ============================================================ + + document.getElementById("copy-btn").addEventListener("click", () => { + const text = document.getElementById("answer-text").textContent; + if (!text) return; + navigator.clipboard.writeText(text).then(() => { + const btn = document.getElementById("copy-btn"); + const original = btn.innerHTML; + btn.innerHTML = ` Copied!`; + setTimeout(() => { btn.innerHTML = original; }, 1800); + }); + }); + + // ============================================================ + // UTILITY + // ============================================================ + + function escapeHtml(str) { + return String(str || "") + .replace(/&/g, "&") + .replace(//g, ">") + .replace(/"/g, """) + .replace(/\n/g, "
"); + } + + /** + * Lightweight markdown β†’ HTML renderer. + * Handles: **bold**, *italic*, `code`, headings (##), bullet lists (* / -), + * numbered lists (1.), and paragraphs. + */ + function renderMarkdown(text) { + if (!text) return ""; + + // Escape HTML entities first + let html = String(text) + .replace(/&/g, "&") + .replace(//g, ">"); + + // Split into lines for block-level processing + const lines = html.split("\n"); + const output = []; + let inList = false; + let listType = null; // 'ul' or 'ol' + + for (let i = 0; i < lines.length; i++) { + let line = lines[i]; + + // Headings + if (/^####\s+(.+)/.test(line)) { + closeList(); + output.push(`

${RegExp.$1}

`); + continue; + } + if (/^###\s+(.+)/.test(line)) { + closeList(); + output.push(`

${RegExp.$1}

`); + continue; + } + if (/^##\s+(.+)/.test(line)) { + closeList(); + output.push(`

${RegExp.$1}

`); + continue; + } + + // Bullet list (* or -) + const bulletMatch = line.match(/^\s*[\*\-]\s+(.+)/); + if (bulletMatch) { + if (listType !== "ul") { closeList(); output.push("
    "); inList = true; listType = "ul"; } + output.push(`
  • ${inlineFormat(bulletMatch[1])}
    • `); + continue; + } + + // Numbered list (1. 2. etc) + const numMatch = line.match(/^\s*(\d+)\.\s+(.+)/); + if (numMatch) { + if (listType !== "ol") { closeList(); output.push("
      "); inList = true; listType = "ol"; } + output.push(`
    1. ${inlineFormat(numMatch[2])}
      2. `); + continue; + } + + // Empty line = paragraph break + if (line.trim() === "") { + closeList(); + output.push("
      "); + continue; + } + + // Regular text line + closeList(); + output.push(`

      ${inlineFormat(line)}

      `); + } + + closeList(); + return output.join("\n"); + + function closeList() { + if (inList) { + output.push(listType === "ol" ? "
    " : "
"); + inList = false; + listType = null; + } + } + } + + /** Inline markdown: **bold**, *italic*, `code` */ + function inlineFormat(text) { + return text + .replace(/\*\*(.+?)\*\*/g, "$1") + .replace(/\*(.+?)\*/g, "$1") + .replace(/`(.+?)`/g, "$1"); + } diff --git a/src/sonic_o1_agent/demo/static/style.css b/src/sonic_o1_agent/demo/static/style.css new file mode 100644 index 0000000..346fb1b --- /dev/null +++ b/src/sonic_o1_agent/demo/static/style.css @@ -0,0 +1,1010 @@ +/* ============================================================ + Sonic O1 Agent β€” Demo UI + Typefaces: Open Sans (display + body) + DM Mono (labels) + Palette: White + Black + Vector Magenta #EB088A + ============================================================ */ + + :root { + --bg: #ffffff; + --bg-2: #ffffff; + --bg-3: #f5f5f5; + --border: #e4e4e0; + --border-2: #d0d0ca; + + --text: #000000; + --text-2: #3c3c3c; + --text-3: #9a9a9a; + + --accent: #EB088A; + --accent-dim: rgba(235,8,138,0.07); + --accent-hover:#c9076f; + + --green: #16a34a; + --green-bg: rgba(22,163,74,0.08); + --green-border:rgba(22,163,74,0.25); + + --amber: #d97706; + --amber-bg: rgba(217,119,6,0.08); + --amber-border:rgba(217,119,6,0.25); + + --red: #d63232; + --red-bg: rgba(214,50,50,0.08); + --red-border: rgba(214,50,50,0.25); + + --blue: #2563eb; + --blue-bg: rgba(37,99,235,0.06); + --blue-border: rgba(37,99,235,0.2); + + --radius: 12px; + --radius-sm: 7px; + --font-display:'Open Sans', sans-serif; + --font-body: 'Open Sans', sans-serif; + --font-mono: 'DM Mono', monospace; + } + + *, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; } + html { scroll-behavior: smooth; } + + body { + background: var(--bg); + color: var(--text); + font-family: var(--font-body); + font-size: 15px; + line-height: 1.6; + min-height: 100vh; + overflow-x: hidden; + } + + .noise { + pointer-events: none; + position: fixed; + inset: 0; + z-index: 1000; + opacity: 0.018; + background-image: url("data:image/svg+xml,%3Csvg viewBox='0 0 256 256' xmlns='http://www.w3.org/2000/svg'%3E%3Cfilter id='noise'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='0.9' numOctaves='4' stitchTiles='stitch'/%3E%3C/filter%3E%3Crect width='100%25' height='100%25' filter='url(%23noise)'/%3E%3C/svg%3E"); + background-size: 200px 200px; + } + + /* ============================================================ + HEADER + ============================================================ */ + + header { + padding: 24px 64px; + border-bottom: 1px solid var(--border); + display: flex; + align-items: center; + justify-content: space-between; + } + + .header-left { + display: flex; + flex-direction: column; + gap: 4px; + } + + .logo-tagline { + font-family: var(--font-body); + font-size: 13px; + color: var(--text-3); + font-weight: 300; + } + + .logo { + font-family: var(--font-display); + font-size: 32px; + font-weight: 800; + letter-spacing: -1px; + line-height: 1; + } + + .logo-sonic { color: var(--text); } + .logo-o1 { color: var(--accent); } + .logo-agent { color: var(--text-2); font-weight: 400; } + + .vector-logo { + height: 64px; + width: auto; + opacity: 0.9; + } + + /* ============================================================ + MAIN + ============================================================ */ + + main { + max-width: 1200px; + margin: 0 auto; + padding: 56px 64px 120px; + display: flex; + flex-direction: column; + gap: 48px; + } + + /* ============================================================ + SECTION LABELS + ============================================================ */ + + .section-label { + display: flex; + align-items: center; + gap: 12px; + margin-bottom: 20px; + } + + .label-num { + font-family: var(--font-mono); + font-size: 11px; + color: var(--accent); + background: var(--accent-dim); + border: 1px solid rgba(235,8,138,0.2); + padding: 2px 8px; + border-radius: 4px; + letter-spacing: 0.05em; + } + + .label-text { + font-family: var(--font-display); + font-size: 13px; + font-weight: 600; + color: var(--text-2); + letter-spacing: 0.06em; + text-transform: uppercase; + } + + /* ============================================================ + UPLOAD ZONE + ============================================================ */ + + .upload-zone { + border: 2px dashed var(--border-2); + border-radius: var(--radius); + padding: 48px 32px; + text-align: center; + cursor: pointer; + transition: border-color 0.2s, background 0.2s; + margin-bottom: 20px; + } + + .upload-zone:hover, + .upload-zone.dragover { + border-color: var(--accent); + background: var(--accent-dim); + } + + .upload-icon { + color: var(--text-3); + margin-bottom: 12px; + transition: color 0.2s; + } + + .upload-zone:hover .upload-icon { color: var(--accent); } + + .upload-label { + font-size: 15px; + color: var(--text-2); + margin-bottom: 6px; + } + + .upload-browse { + color: var(--accent); + font-weight: 600; + cursor: pointer; + } + + .upload-hint { + font-family: var(--font-mono); + font-size: 12px; + color: var(--text-3); + } + + /* FILE PREVIEW */ + + .file-preview { + display: flex; + align-items: center; + justify-content: space-between; + background: var(--accent-dim); + border: 1px solid rgba(235,8,138,0.2); + border-radius: var(--radius-sm); + padding: 12px 16px; + margin-bottom: 20px; + } + + .file-preview-info { + display: flex; + align-items: center; + gap: 10px; + } + + .file-name { + font-family: var(--font-mono); + font-size: 13px; + font-weight: 500; + color: var(--text); + } + + .file-size { + font-family: var(--font-mono); + font-size: 12px; + color: var(--text-3); + } + + .file-remove { + background: none; + border: none; + color: var(--text-3); + font-size: 16px; + cursor: pointer; + padding: 4px 8px; + border-radius: 4px; + transition: color 0.15s, background 0.15s; + } + + .file-remove:hover { + color: var(--red); + background: var(--red-bg); + } + + /* ============================================================ + EXAMPLE CHIPS + ============================================================ */ + + .examples { + display: flex; + align-items: center; + gap: 8px; + margin-bottom: 14px; + flex-wrap: wrap; + } + + .examples-label { + font-family: var(--font-mono); + font-size: 11px; + color: var(--text-3); + text-transform: uppercase; + letter-spacing: 0.06em; + white-space: nowrap; + margin-right: 2px; + } + + .example-chip { + font-family: var(--font-mono); + font-size: 12px; + color: var(--text-2); + background: var(--bg-3); + border: 1px solid var(--border); + border-radius: 20px; + padding: 5px 14px; + cursor: pointer; + transition: border-color 0.15s, color 0.15s, background 0.15s; + white-space: nowrap; + } + + .example-chip:hover { + border-color: var(--accent); + color: var(--accent); + background: var(--accent-dim); + } + + /* ============================================================ + TEXTAREA + FOOTER + ============================================================ */ + + .input-section { animation: fadeUp 0.5s ease both; } + + textarea { + width: 100%; + min-height: 140px; + background: var(--bg-2); + border: 1px solid var(--border); + border-radius: var(--radius); + color: var(--text); + font-family: var(--font-body); + font-size: 15px; + line-height: 1.7; + padding: 24px 28px; + resize: vertical; + outline: none; + transition: border-color 0.2s, box-shadow 0.2s; + } + + textarea::placeholder { color: var(--text-3); } + + textarea:focus { + border-color: var(--accent); + box-shadow: 0 0 0 3px rgba(235,8,138,0.08); + } + + .input-footer { + display: flex; + align-items: center; + justify-content: space-between; + margin-top: 12px; + position: relative; + } + + .disclaimer-inline { + position: absolute; + left: 50%; + transform: translateX(-50%); + font-family: var(--font-mono); + font-size: 0.72rem; + color: rgba(0,0,0,0.3); + } + + .char-count { + font-family: var(--font-mono); + font-size: 12px; + color: var(--text-3); + } + + .char-count.warn { color: var(--amber); } + .char-count.error { color: var(--red); } + + /* ============================================================ + ANALYZE BUTTON + ============================================================ */ + + .analyze-btn { + display: flex; + align-items: center; + gap: 10px; + background: var(--text); + color: #ffffff; + border: none; + border-radius: var(--radius-sm); + font-family: var(--font-display); + font-size: 14px; + font-weight: 700; + padding: 12px 24px; + cursor: pointer; + transition: transform 0.15s, box-shadow 0.15s, background 0.15s; + letter-spacing: 0.01em; + } + + .analyze-btn:hover:not(:disabled) { + background: var(--accent); + transform: translateY(-1px); + box-shadow: 0 6px 24px rgba(235,8,138,0.25); + } + + .analyze-btn:active { transform: translateY(0); } + + .analyze-btn:disabled { + opacity: 0.35; + cursor: not-allowed; + transform: none; + box-shadow: none; + background: var(--text); + } + + .btn-arrow { + font-size: 18px; + transition: transform 0.15s; + } + + .analyze-btn:hover:not(:disabled) .btn-arrow { transform: translateX(3px); } + + /* ============================================================ + PIPELINE PROGRESS + ============================================================ */ + + .pipeline { animation: fadeUp 0.4s ease both; } + + .pipeline-track { + display: flex; + flex-direction: column; + gap: 0; + } + + .pipeline-node { + display: flex; + align-items: center; + gap: 16px; + padding: 14px 20px; + border-radius: var(--radius-sm); + transition: background 0.2s; + } + + .pipeline-node.active { + background: var(--accent-dim); + } + + .pipeline-node.done .node-dot { + background: var(--accent); + border-color: var(--accent); + box-shadow: 0 0 0 3px rgba(235,8,138,0.15); + } + + .pipeline-node.done .node-dot::after { + content: "βœ“"; + color: #fff; + font-size: 10px; + font-weight: 700; + } + + .pipeline-node.active .node-dot { + border-color: var(--accent); + animation: pulse 1.2s ease-in-out infinite; + } + + .node-indicator { + flex-shrink: 0; + width: 28px; + height: 28px; + display: flex; + align-items: center; + justify-content: center; + } + + .node-dot { + width: 22px; + height: 22px; + border-radius: 50%; + border: 2px solid var(--border-2); + background: var(--bg); + display: flex; + align-items: center; + justify-content: center; + transition: all 0.3s; + } + + .node-info { + display: flex; + flex-direction: column; + gap: 2px; + flex: 1; + } + + .node-name { + font-family: var(--font-display); + font-size: 14px; + font-weight: 600; + color: var(--text); + } + + .node-desc { + font-family: var(--font-mono); + font-size: 11px; + color: var(--text-3); + } + + .node-time { + font-family: var(--font-mono); + font-size: 11px; + color: var(--text-3); + min-width: 48px; + text-align: right; + } + + .pipeline-connector { + width: 2px; + height: 12px; + background: var(--border); + margin-left: 30px; + transition: background 0.3s; + } + + .pipeline-connector.done { + background: var(--accent); + } + + @keyframes pulse { + 0%, 100% { box-shadow: 0 0 0 0 rgba(235,8,138,0.3); } + 50% { box-shadow: 0 0 0 8px rgba(235,8,138,0); } + } + + /* ============================================================ + THINKING PANEL (live progress during pipeline) + ============================================================ */ + + .thinking-panel { + margin-top: 20px; + background: var(--bg-2); + border: 1px solid var(--border); + border-radius: var(--radius); + overflow: hidden; + } + + .thinking-header { + padding: 12px 20px; + border-bottom: 1px solid var(--border); + display: flex; + align-items: center; + gap: 8px; + } + + .thinking-title { + font-family: var(--font-display); + font-size: 13px; + font-weight: 600; + color: var(--text-2); + } + + .thinking-log { + padding: 16px 20px; + max-height: 400px; + overflow-y: auto; + display: flex; + flex-direction: column; + gap: 10px; + } + + .thinking-entry { + display: flex; + gap: 10px; + align-items: flex-start; + animation: fadeUp 0.3s ease both; + } + + .thinking-badge { + font-family: var(--font-mono); + font-size: 10px; + font-weight: 500; + text-transform: uppercase; + letter-spacing: 0.06em; + padding: 3px 8px; + border-radius: 4px; + white-space: nowrap; + flex-shrink: 0; + margin-top: 2px; + } + + .thinking-badge.planning { color: var(--accent); background: var(--accent-dim); border: 1px solid rgba(235,8,138,0.2); } + .thinking-badge.processing { color: var(--blue); background: var(--blue-bg); border: 1px solid var(--blue-border); } + .thinking-badge.inference { color: var(--green); background: var(--green-bg); border: 1px solid var(--green-border); } + .thinking-badge.reflection { color: var(--amber); background: var(--amber-bg); border: 1px solid var(--amber-border); } + + .thinking-text { + font-family: var(--font-mono); + font-size: 12px; + color: var(--text-2); + line-height: 1.6; + word-break: break-word; + } + + .thinking-text strong { + color: var(--text); + font-weight: 600; + } + + .thinking-block { + font-family: var(--font-body); + font-size: 13px; + color: var(--text); + line-height: 1.7; + background: var(--bg-3); + border: 1px solid var(--border); + border-radius: var(--radius-sm); + padding: 12px 16px; + word-break: break-word; + max-height: 300px; + overflow-y: auto; + flex: 1; + } + + /* ============================================================ + RESULTS + ============================================================ */ + + .results { + display: flex; + flex-direction: column; + gap: 40px; + animation: fadeUp 0.5s ease both; + } + + /* ANSWER CARD */ + + .answer-card { + background: var(--bg-2); + border: 1px solid var(--border); + border-radius: var(--radius); + overflow: hidden; + position: relative; + } + + .answer-header { + padding: 16px 24px; + border-bottom: 1px solid var(--border); + display: flex; + align-items: center; + justify-content: space-between; + } + + .answer-title { + font-family: var(--font-display); + font-size: 14px; + font-weight: 700; + color: var(--accent); + } + + .confidence-badge { + display: flex; + align-items: center; + gap: 8px; + } + + .confidence-label { + font-family: var(--font-mono); + font-size: 10px; + color: var(--text-3); + text-transform: uppercase; + letter-spacing: 0.06em; + } + + .confidence-value { + font-family: var(--font-mono); + font-size: 13px; + font-weight: 500; + padding: 3px 10px; + border-radius: 20px; + border: 1px solid var(--border); + } + + .confidence-value.high { color: var(--green); background: var(--green-bg); border-color: var(--green-border); } + .confidence-value.medium { color: var(--amber); background: var(--amber-bg); border-color: var(--amber-border); } + .confidence-value.low { color: var(--red); background: var(--red-bg); border-color: var(--red-border); } + + .answer-body { + padding: 24px; + font-size: 15px; + line-height: 1.8; + color: var(--text); + word-break: break-word; + } + + /* Markdown rendered elements */ + .answer-body .md-p, + .reasoning-step-body .md-p, + .thinking-block .md-p { + margin: 0 0 8px 0; + } + + .answer-body .md-p:last-child, + .reasoning-step-body .md-p:last-child, + .thinking-block .md-p:last-child { + margin-bottom: 0; + } + + .md-h2 { + font-family: var(--font-display); + font-size: 18px; + font-weight: 700; + color: var(--text); + margin: 16px 0 8px 0; + } + + .md-h3 { + font-family: var(--font-display); + font-size: 16px; + font-weight: 700; + color: var(--text); + margin: 12px 0 6px 0; + } + + .md-h4 { + font-family: var(--font-display); + font-size: 14px; + font-weight: 700; + color: var(--text-2); + margin: 10px 0 4px 0; + } + + .md-ul, .md-ol { + margin: 8px 0; + padding-left: 24px; + } + + .md-ul li, .md-ol li { + margin-bottom: 6px; + line-height: 1.7; + } + + .md-code { + font-family: var(--font-mono); + font-size: 0.9em; + background: var(--bg-3); + border: 1px solid var(--border); + border-radius: 3px; + padding: 1px 5px; + } + + .copy-btn { + position: absolute; + top: 12px; + right: 16px; + display: flex; + align-items: center; + gap: 6px; + background: var(--bg-3); + border: 1px solid var(--border); + border-radius: var(--radius-sm); + color: var(--text-2); + font-family: var(--font-mono); + font-size: 11px; + padding: 5px 10px; + cursor: pointer; + transition: border-color 0.15s, color 0.15s, background 0.15s; + } + + .copy-btn:hover { + border-color: var(--accent); + color: var(--accent); + background: var(--accent-dim); + } + + /* ============================================================ + PLAN LIST + ============================================================ */ + + .plan-list { + display: flex; + flex-direction: column; + gap: 10px; + } + + .plan-step { + display: flex; + align-items: flex-start; + gap: 14px; + padding: 16px 20px; + background: var(--bg-2); + border: 1px solid var(--border); + border-radius: var(--radius-sm); + border-left: 3px solid var(--accent); + } + + .plan-step-num { + font-family: var(--font-mono); + font-size: 11px; + color: var(--accent); + background: var(--accent-dim); + border: 1px solid rgba(235,8,138,0.2); + padding: 2px 8px; + border-radius: 4px; + white-space: nowrap; + margin-top: 1px; + } + + .plan-step-text { + font-size: 14px; + color: var(--text); + line-height: 1.6; + } + + /* ============================================================ + REASONING CHAIN + ============================================================ */ + + .reasoning-list { + display: flex; + flex-direction: column; + gap: 10px; + } + + .reasoning-step { + padding: 16px 20px; + background: var(--bg-2); + border: 1px solid var(--border); + border-radius: var(--radius-sm); + border-left: 3px solid var(--blue); + } + + .reasoning-step-header { + display: flex; + align-items: center; + gap: 10px; + margin-bottom: 8px; + } + + .reasoning-step-badge { + font-family: var(--font-mono); + font-size: 10px; + color: var(--blue); + background: var(--blue-bg); + border: 1px solid var(--blue-border); + padding: 2px 8px; + border-radius: 4px; + text-transform: uppercase; + letter-spacing: 0.06em; + } + + .reasoning-step-action { + font-family: var(--font-display); + font-size: 13px; + font-weight: 600; + color: var(--text); + } + + .reasoning-step-body { + font-size: 13px; + color: var(--text-2); + line-height: 1.6; + } + + /* ============================================================ + REFLECTION / SCORES + ============================================================ */ + + .reflection-grid { + display: flex; + flex-direction: column; + gap: 16px; + } + + .score-cards { + display: grid; + grid-template-columns: repeat(4, 1fr); + gap: 12px; + } + + .score-card { + background: var(--bg-2); + border: 1px solid var(--border); + border-radius: var(--radius-sm); + padding: 16px; + text-align: center; + } + + .score-card-label { + font-family: var(--font-mono); + font-size: 10px; + color: var(--text-3); + text-transform: uppercase; + letter-spacing: 0.08em; + display: block; + margin-bottom: 8px; + } + + .score-card-value { + font-family: var(--font-display); + font-size: 28px; + font-weight: 800; + line-height: 1; + } + + .score-card-value.high { color: var(--green); } + .score-card-value.medium { color: var(--amber); } + .score-card-value.low { color: var(--red); } + + .score-card-bar { + width: 100%; + height: 3px; + background: var(--bg-3); + border-radius: 2px; + margin-top: 10px; + overflow: hidden; + } + + .score-card-fill { + height: 100%; + border-radius: 2px; + transition: width 0.6s ease; + } + + .score-card-fill.high { background: var(--green); } + .score-card-fill.medium { background: var(--amber); } + .score-card-fill.low { background: var(--red); } + + /* HALLUCINATION */ + + .hallucination-card { + display: flex; + align-items: center; + justify-content: space-between; + padding: 14px 20px; + background: var(--bg-2); + border: 1px solid var(--border); + border-radius: var(--radius-sm); + } + + .hallucination-label { + font-family: var(--font-mono); + font-size: 11px; + color: var(--text-3); + text-transform: uppercase; + letter-spacing: 0.06em; + } + + .hallucination-value { + font-family: var(--font-mono); + font-size: 12px; + font-weight: 500; + padding: 3px 10px; + border-radius: 20px; + } + + .hallucination-value.none { color: var(--green); background: var(--green-bg); border: 1px solid var(--green-border); } + .hallucination-value.low { color: var(--amber); background: var(--amber-bg); border: 1px solid var(--amber-border); } + .hallucination-value.medium { color: var(--amber); background: var(--amber-bg); border: 1px solid var(--amber-border); } + .hallucination-value.high { color: var(--red); background: var(--red-bg); border: 1px solid var(--red-border); } + + /* REFINEMENT */ + + .refinement-section { margin-top: 4px; } + + .refinement-label { + font-family: var(--font-mono); + font-size: 11px; + color: var(--text-3); + text-transform: uppercase; + letter-spacing: 0.06em; + display: block; + margin-bottom: 10px; + } + + .refinement-list { + display: flex; + flex-direction: column; + gap: 8px; + } + + .refinement-item { + display: flex; + align-items: center; + gap: 12px; + padding: 10px 16px; + background: var(--bg-3); + border-radius: var(--radius-sm); + font-family: var(--font-mono); + font-size: 12px; + } + + .refinement-attempt { + color: var(--text-3); + min-width: 72px; + } + + .refinement-confidence { + font-weight: 500; + } + + .refinement-rec { + margin-left: auto; + font-size: 10px; + text-transform: uppercase; + letter-spacing: 0.06em; + padding: 2px 8px; + border-radius: 4px; + } + + .refinement-rec.accept { color: var(--green); background: var(--green-bg); } + .refinement-rec.refine { color: var(--amber); background: var(--amber-bg); } + .refinement-rec.reject { color: var(--red); background: var(--red-bg); } + + /* ============================================================ + UTILITIES + ============================================================ */ + + .hidden { display: none !important; } + + @keyframes fadeUp { + from { opacity: 0; transform: translateY(12px); } + to { opacity: 1; transform: translateY(0); } + } + + /* ============================================================ + RESPONSIVE + ============================================================ */ + + @media (max-width: 900px) { + header { padding: 20px 24px; } + main { padding: 40px 24px 80px; } + .score-cards { grid-template-columns: repeat(2, 1fr); } + .examples { gap: 6px; } + .examples-label { display: none; } + .logo-tagline { display: none; } + } + + .site-footer { + width: 100%; + text-align: center; + padding: 2rem; + font-family: var(--font-mono); + font-size: 0.72rem; + color: rgba(0,0,0,0.25); + border-top: 1px solid rgba(0,0,0,0.08); + margin-top: 4rem; + } diff --git a/src/sonic_o1_agent/demo/templates/index.html b/src/sonic_o1_agent/demo/templates/index.html new file mode 100644 index 0000000..40b6dc7 --- /dev/null +++ b/src/sonic_o1_agent/demo/templates/index.html @@ -0,0 +1,250 @@ + + + + + + Sonic O1 Agent | Vector Institute + + + + + + + +
+ +
+
+ + Multi-agent video & audio understanding +
+ +
+ +
+ + +
+
+ 01 + Upload & Ask +
+ + +
+
+ + + + + + +
+

Drop a video file here or browse

+

MP4, MOV, AVI, MKV β€” max 30 minutes

+ +
+ + + + + +
+ Example queries + + + + +
+ + + + +
+ 0 / 2000 + ⚠ Results are AI-generated and should be verified against the source material. + +
+
+ + + + + + + +
+ +
Copyright Β© 2026, Vector AI Engineering
+ + + + diff --git a/src/sonic_o1_agent/models/__init__.py b/src/sonic_o1_agent/models/__init__.py new file mode 100644 index 0000000..bb7b15c --- /dev/null +++ b/src/sonic_o1_agent/models/__init__.py @@ -0,0 +1,8 @@ +"""Model wrappers for multimodal inference. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from sonic_o1_agent.models.qwen_model import Qwen3OmniModel + +__all__ = ["Qwen3OmniModel"] diff --git a/src/sonic_o1_agent/models/qwen_model.py b/src/sonic_o1_agent/models/qwen_model.py new file mode 100644 index 0000000..72e9040 --- /dev/null +++ b/src/sonic_o1_agent/models/qwen_model.py @@ -0,0 +1,962 @@ +"""Qwen3-Omni model wrapper with vLLM for efficient inference. + +Supports two backends: + 1. **Embedded vLLM** (default) β€” loads model in-process via ``vllm.LLM``. + This is the original behaviour; nothing changes for existing callers. + 2. **vLLM server** β€” connects to a running ``vllm serve`` instance via the + OpenAI-compatible API. Activated when ``vllm_base_url`` is set in config + or via the ``VLLM_BASE_URL`` environment variable. + +The public API (``load``, ``generate``, ``unload``, etc.) is identical +regardless of which backend is active. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import base64 +import gc +import logging +import multiprocessing +import os +import shutil +import time +from pathlib import Path +from typing import Any, Dict, List, Optional, Tuple + +import torch +import torch.distributed as dist + +from sonic_o1_agent.core.multimodal_engine import process_mm_info + +logger = logging.getLogger(__name__) + + +class Qwen3OmniModel: + """Qwen3-Omni wrapper with vLLM for efficient multi-GPU inference. + + Supports Instruct and Thinking variants with audio chunking. + Supports text-only mode for reasoning steps. + Supports remote vLLM server via OpenAI SDK when ``vllm_base_url`` is set. + """ + + def __init__(self, config: Dict[str, Any]) -> None: + """Initialize Qwen3-Omni model. + + Args: + config: Configuration dictionary with model settings. + Optional key ``vllm_base_url`` (str) switches to server mode. + """ + self.model_path = config.get("model_path", "Qwen/Qwen3-Omni-30B-A3B-Instruct") + self.use_thinking = config.get("use_thinking", False) + + self.gpu_memory_utilization = config.get("gpu_memory_utilization", 0.85) + self.tensor_parallel_size = config.get( + "tensor_parallel_size", torch.cuda.device_count() + ) + self.max_num_seqs = config.get("max_num_seqs", 1) + self.max_model_len = config.get("max_model_len", 65536) + + gen_config = config.get("generation_config", {}) + self.temperature = gen_config.get("temperature", 0.0) + self.top_p = gen_config.get("top_p", 0.95) + self.top_k = gen_config.get("top_k", 20) + self.max_tokens = gen_config.get("max_new_tokens", 8192) + + self.default_max_frames = config.get("max_frames", 256) + self.default_min_frames = config.get("min_frames", 64) + + self.audio_feature_rate = 25.0 + + self.limit_mm_per_prompt = config.get( + "limit_mm_per_prompt", {"image": 1, "video": 1, "audio": 1} + ) + + # --- Backend selection ------------------------------------------------ + # Server mode: connect to a running vLLM serve instance. + # Embedded mode: load model in-process (original behaviour). + self.vllm_base_url: str = config.get( + "vllm_base_url", os.environ.get("VLLM_BASE_URL", "") + ) + self._server_mode = bool(self.vllm_base_url) + + # Server-mode client (lazy β€” created in load()) + self._client: Optional[Any] = None # openai.OpenAI + + # Embedded-mode objects (lazy β€” created in load()) + self.llm: Optional[Any] = None # vllm.LLM + self.processor: Optional[Any] = None # Qwen3OmniMoeProcessor + + self.stats = { + "total_samples": 0, + "audio_chunks_sampled": 0, + } + + if self._server_mode: + logger.info( + "Qwen3OmniModel: server mode enabled (vLLM @ %s)", self.vllm_base_url + ) + else: + logger.info("Qwen3OmniModel: embedded mode (in-process vLLM)") + + # ====================================================================== + # LOAD / UNLOAD + # ====================================================================== + + def load(self) -> None: + """Load the model backend. + + - Server mode: creates an ``openai.OpenAI`` client. No weights loaded. + - Embedded mode: loads model via ``vllm.LLM`` (original behaviour). + """ + if self._server_mode: + self._load_server() + else: + self._load_embedded() + + def _load_server(self) -> None: + """Create OpenAI client for the remote vLLM server.""" + from openai import OpenAI + + self._client = OpenAI( + base_url=self.vllm_base_url, + api_key=os.environ.get("VLLM_API_KEY", "EMPTY"), + ) + mode = "Thinking" if self.use_thinking else "Instruct" + logger.info( + "Connected to vLLM server at %s (model: %s, mode: %s)", + self.vllm_base_url, + self.model_path, + mode, + ) + + def _load_embedded(self) -> None: + """Load model weights in-process via vllm.LLM (original path).""" + from transformers import Qwen3OmniMoeProcessor + from vllm import LLM # noqa: F811 + + try: + self._clear_vllm_cache() + + # Suppress verbose vLLM logging + vllm_loggers = [ + "vllm", + "vllm.engine", + "vllm.worker", + "vllm.multiproc_executor", + "vllm.parallel_state", + "vllm.config", + "vllm.engine.arg_utils", + ] + for logger_name in vllm_loggers: + logging.getLogger(logger_name).setLevel(logging.WARNING) + + os.environ["VLLM_USE_V1"] = "0" + os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn" + os.environ["VLLM_LOGGING_LEVEL"] = "WARNING" + if self.max_model_len > 65536: + os.environ["VLLM_ALLOW_LONG_MAX_MODEL_LEN"] = "1" + + logger.info(f"Loading Qwen3-Omni model from {self.model_path} with vLLM") + logger.info( + f"Using {self.tensor_parallel_size} GPUs for tensor parallelism" + ) + logger.info(f"Context length: {self.max_model_len} tokens") + + self.llm = LLM( + model=self.model_path, + trust_remote_code=True, + gpu_memory_utilization=self.gpu_memory_utilization, + tensor_parallel_size=self.tensor_parallel_size, + limit_mm_per_prompt=self.limit_mm_per_prompt, + max_num_seqs=self.max_num_seqs, + max_model_len=self.max_model_len, + seed=1234, + disable_log_stats=True, + enforce_eager=False, + enable_prefix_caching=True, + mm_processor_kwargs={"cache_gb": 0}, + ) + + self.processor = Qwen3OmniMoeProcessor.from_pretrained(self.model_path) + + mode = "Thinking" if self.use_thinking else "Instruct" + logger.info(f"Successfully loaded Qwen3-Omni with vLLM ({mode} mode)") + + except Exception as e: + raise RuntimeError(f"Failed to load Qwen3-Omni model with vLLM: {e}") + + def unload(self) -> None: + """Release resources. + + - Server mode: drops the client reference. + - Embedded mode: full GPU cleanup (original behaviour). + """ + if self._server_mode: + self._client = None + logger.info("Server client released") + return + + # --- Embedded cleanup (unchanged) --- + if self.llm is not None: + try: + del self.llm + except Exception as e: + logger.warning(f"Error deleting llm object: {e}") + self.llm = None + + if self.processor is not None: + del self.processor + self.processor = None + + gc.collect() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + torch.cuda.synchronize() + for i in range(torch.cuda.device_count()): + with torch.cuda.device(i): + torch.cuda.empty_cache() + torch.cuda.synchronize() + + if dist.is_initialized(): + try: + dist.destroy_process_group() + logger.info("Distributed process group destroyed") + except Exception as e: + logger.warning(f"Failed to destroy process group: {e}") + + try: + active_children = multiprocessing.active_children() + if active_children: + logger.info( + f"Found {len(active_children)} active child processes. " + "Terminating..." + ) + for child in active_children: + try: + child.terminate() + child.join(timeout=0.5) + if child.is_alive(): + child.kill() + except Exception as e: + logger.warning(f"Failed to kill child {child.pid}: {e}") + except Exception as e: + logger.warning(f"Error during manual process cleanup: {e}") + + logger.info("Model unloaded, memory cleared, and child processes terminated") + + # ====================================================================== + # GENERATE β€” main entry point + # ====================================================================== + + def generate( + self, + video_path: Optional[str], + audio_path: Optional[str], + prompt: str, + max_frames: Optional[int] = None, + max_audio_chunks: Optional[int] = None, + **kwargs, + ) -> Tuple[str, Dict[str, Any]]: + """Generate response from video and/or audio. + + Routes to the server or embedded backend transparently. + Signature and return type are identical for both backends. + + Args: + video_path: Video file path or None + audio_path: Audio file path or None + prompt: Text prompt + max_frames: Maximum frames to use + max_audio_chunks: Maximum audio chunks + **kwargs: Additional generation parameters + + Returns: + Tuple of (generated_text, metadata_dict) + """ + if self._server_mode: + return self._generate_via_server( + video_path, audio_path, prompt, max_frames, max_audio_chunks, **kwargs + ) + else: + return self._generate_embedded( + video_path, audio_path, prompt, max_frames, max_audio_chunks, **kwargs + ) + + # ====================================================================== + # SERVER MODE β€” OpenAI SDK path + # ====================================================================== + + def _ensure_server_client(self) -> None: + """Ensure the OpenAI client is available, lazy-load if needed.""" + if self._client is None: + self._load_server() + assert self._client is not None, ( + "Server client not initialised. Is vllm_base_url set?" + ) + + def _generate_via_server( + self, + video_path: Optional[str], + audio_path: Optional[str], + prompt: str, + max_frames: Optional[int] = None, + max_audio_chunks: Optional[int] = None, + **kwargs, + ) -> Tuple[str, Dict[str, Any]]: + """Generate response via the remote vLLM server (OpenAI SDK). + + Performs **client-side frame extraction** using the same + ``video_processor`` as the embedded path, then sends pre-sampled + frames as base64 JPEGs to the vLLM server. This gives full + control over frame count (``max_frames`` / ``min_frames`` from + config) and avoids vLLM's default 32-frame sampling and frame + mismatch errors. + + Audio is sent as base64 ``input_audio``. + + Supports ``video_start``/``video_end``/``audio_start``/``audio_end`` + kwargs for segment-level processing. + + Args: + video_path: Local video file path or None + audio_path: Local audio file path or None + prompt: Text prompt + max_frames: Maximum video frames to sample + max_audio_chunks: Not used in server mode + **kwargs: Additional params (temperature, top_p, video_start, + video_end, audio_start, audio_end, etc.) + + Returns: + Tuple of (generated_text, metadata_dict) + """ + self._ensure_server_client() + + has_video = video_path is not None and Path(video_path).exists() + has_audio = audio_path is not None and Path(audio_path).exists() + + # Text-only mode (reasoning steps) + if not has_video and not has_audio: + return self._generate_text_only_server(prompt, kwargs) + + # --- Build multimodal content ------------------------------------ + content: List[Dict[str, Any]] = [] + metadata: Dict[str, Any] = {} + + # --- Video: client-side frame extraction ------------------------- + if has_video: + assert video_path is not None + actual_max_frames = max_frames or self.default_max_frames + actual_min_frames = min(self.default_min_frames, actual_max_frames) + + video_start = kwargs.get("video_start") + video_end = kwargs.get("video_end") + + try: + frames_b64, video_meta = self._extract_frames_for_server( + video_path=video_path, + max_frames=actual_max_frames, + min_frames=actual_min_frames, + video_start=video_start, + video_end=video_end, + ) + + # Send frames as video/jpeg (vLLM treats this as pre-extracted) + content.append( + { + "type": "video_url", + "video_url": { + "url": f"data:video/jpeg;base64,{','.join(frames_b64)}", + }, + } + ) + metadata["video_metadata"] = [video_meta] + logger.info( + "Server mode: %d frames extracted (max=%d, %s)", + video_meta.get("frames_sampled", 0), + actual_max_frames, + f"{video_start:.0f}s-{video_end:.0f}s" + if video_start is not None + else "full", + ) + except Exception as e: + logger.warning("Frame extraction failed: %s β€” sending raw video", e) + # Fallback: send raw video as base64 + try: + video_b64 = self._encode_video_base64(video_path) + content.append( + { + "type": "video_url", + "video_url": {"url": f"data:video/mp4;base64,{video_b64}"}, + } + ) + except Exception as e2: + logger.error("Video encoding also failed: %s", e2) + + # --- Audio: base64 ----------------------------------------------- + if has_audio: + assert audio_path is not None + audio_start = kwargs.get("audio_start") + audio_end = kwargs.get("audio_end") + actual_audio = audio_path + + # Cut audio segment if time range specified + temp_audio = None + if audio_start is not None and audio_end is not None: + actual_audio = self._ffmpeg_cut_audio( + audio_path, audio_start, audio_end + ) + if actual_audio != audio_path: + temp_audio = actual_audio + + try: + audio_b64 = self._encode_audio_base64(actual_audio) + audio_ext = Path(actual_audio).suffix.lstrip(".") or "wav" + content.append( + { + "type": "input_audio", + "input_audio": {"data": audio_b64, "format": audio_ext}, + } + ) + metadata["audio_path"] = actual_audio + logger.info("Server mode: audio via base64 (%s)", audio_ext) + except Exception as e: + logger.warning("Audio encoding failed: %s β€” skipping", e) + finally: + if temp_audio: + try: + Path(temp_audio).unlink(missing_ok=True) + except Exception: + pass + + # --- Text prompt ------------------------------------------------- + content.append({"type": "text", "text": prompt}) + messages = [{"role": "user", "content": content}] + + # --- Send to vLLM ------------------------------------------------ + temperature = kwargs.get("temperature", self.temperature) + top_p = kwargs.get("top_p", self.top_p) + max_tokens = kwargs.get("max_new_tokens", self.max_tokens) + + self.stats["total_samples"] += 1 + + try: + logger.info("Server mode: sending request to vLLM...") + assert self._client is not None + + completion = self._client.chat.completions.create( + model=self.model_path, + messages=messages, + max_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + ) + + response_text = completion.choices[0].message.content or "" + logger.info("Server mode: received response (%d chars)", len(response_text)) + return response_text, metadata + + except Exception as e: + logger.error("Server mode generation failed: %s", e, exc_info=True) + raise RuntimeError(f"Server mode generation failed: {e}") + + def _extract_frames_for_server( + self, + video_path: str, + max_frames: int, + min_frames: int, + video_start: Optional[float] = None, + video_end: Optional[float] = None, + ) -> Tuple[List[str], Dict[str, Any]]: + """Extract and encode video frames client-side for the vLLM server. + + Uses the same frame sampling logic as the embedded mode + (``process_video_with_metadata``) but returns base64-encoded + JPEG strings instead of tensors. + + Args: + video_path: Path to video file. + max_frames: Maximum frames to sample. + min_frames: Minimum frames to sample. + video_start: Start time in seconds (optional). + video_end: End time in seconds (optional). + + Returns: + Tuple of (list of base64 JPEG strings, metadata dict). + """ + import io + + from PIL import Image + + from sonic_o1_agent.core.video_processor import process_video_with_metadata + + frames_tensor, video_meta = process_video_with_metadata( + video_path=video_path, + max_frames=max_frames, + min_frames=min_frames, + video_start=video_start, + video_end=video_end, + ) + + # Convert tensor frames to base64 JPEGs + frames_b64: List[str] = [] + # frames_tensor shape: (N, C, H, W) float + for i in range(frames_tensor.shape[0]): + frame = frames_tensor[i].permute(1, 2, 0).byte().numpy() + img = Image.fromarray(frame) + buf = io.BytesIO() + img.save(buf, format="JPEG", quality=85) + b64 = base64.b64encode(buf.getvalue()).decode("utf-8") + frames_b64.append(b64) + + logger.info( + "Extracted %d frames from %s (max=%d, min=%d)", + len(frames_b64), + video_path, + max_frames, + min_frames, + ) + + return frames_b64, video_meta + + @staticmethod + def _ffmpeg_cut_audio( + input_path: str, + start_sec: float, + end_sec: float, + ) -> str: + """Cut an audio segment using ffmpeg. Returns temp path or original.""" + import subprocess + import tempfile + + duration = end_sec - start_sec + if duration <= 0: + return input_path + + suffix = Path(input_path).suffix or ".wav" + + try: + tmp = tempfile.NamedTemporaryFile( + suffix=suffix, prefix="sonic_aseg_", delete=False + ) + tmp_path = tmp.name + tmp.close() + + subprocess.run( + [ + "ffmpeg", + "-y", + "-ss", + str(start_sec), + "-i", + input_path, + "-t", + str(duration), + "-c", + "copy", + "-avoid_negative_ts", + "1", + tmp_path, + ], + capture_output=True, + timeout=60, + check=True, + ) + return tmp_path + + except Exception as e: + logger.warning( + "Audio cut failed for %.1fs-%.1fs: %s β€” sending full audio", + start_sec, + end_sec, + e, + ) + return input_path + + @staticmethod + def _encode_video_base64(video_path: str) -> str: + """Read a video file and return its base64-encoded content (fallback).""" + with open(video_path, "rb") as f: + return base64.b64encode(f.read()).decode("utf-8") + + def _generate_text_only_server( + self, prompt: str, kwargs: Dict + ) -> Tuple[str, Dict[str, Any]]: + """Text-only generation via server (for reasoning steps).""" + self._ensure_server_client() + + temperature = kwargs.get("temperature", self.temperature) + top_p = kwargs.get("top_p", self.top_p) + max_tokens = kwargs.get("max_new_tokens", self.max_tokens) + + logger.info("Server mode: text-only generation...") + assert self._client is not None + + try: + completion = self._client.chat.completions.create( + model=self.model_path, + messages=[ + { + "role": "user", + "content": [{"type": "text", "text": prompt}], + } + ], + max_tokens=max_tokens, + temperature=temperature, + top_p=top_p, + extra_body={"modalities": ["text"]}, + ) + + response_text = completion.choices[0].message.content or "" + logger.info( + "Server mode: text-only response (%d chars)", len(response_text) + ) + return response_text, {} + + except Exception as e: + logger.error("Server text-only generation failed: %s", e, exc_info=True) + raise RuntimeError(f"Server text-only generation failed: {e}") + + @staticmethod + def _encode_audio_base64(audio_path: str) -> str: + """Read an audio file and return its base64-encoded content.""" + with open(audio_path, "rb") as f: + return base64.b64encode(f.read()).decode("utf-8") + + # ====================================================================== + # EMBEDDED MODE β€” original in-process vLLM path (UNCHANGED) + # ====================================================================== + + def _generate_embedded( + self, + video_path: Optional[str], + audio_path: Optional[str], + prompt: str, + max_frames: Optional[int] = None, + max_audio_chunks: Optional[int] = None, + **kwargs, + ) -> Tuple[str, Dict[str, Any]]: + """Generate response using embedded vLLM engine (original code). + + This method is the original ``generate()`` body, completely unchanged. + """ + from vllm import SamplingParams + + if self.llm is None or self.processor is None: + try: + logger.warning( + "Model found unloaded in generate(), attempting lazy load..." + ) + self.load() + except Exception: + raise RuntimeError("Model not loaded. Call load() first.") + + assert self.llm is not None + assert self.processor is not None + + # Determine modality mode + has_video = video_path is not None + has_audio = audio_path is not None + + # Allow text-only mode for reasoning steps + if not has_video and not has_audio: + modality_mode = "text-only" + logger.info(f"Modality mode: {modality_mode}") + return self._generate_text_only(prompt, kwargs) + elif has_video and has_audio: + modality_mode = "video+audio" + elif has_video: + modality_mode = "video-only" + else: + modality_mode = "audio-only" + + logger.info(f"Modality mode: {modality_mode}") + + if has_video: + assert video_path is not None + video_path_obj = Path(video_path) + if not video_path_obj.exists(): + raise FileNotFoundError(f"Video file not found: {video_path_obj}") + + actual_max_frames = ( + max_frames if max_frames is not None else self.default_max_frames + ) + + try: + logger.info( + f"Processing: frames={actual_max_frames if has_video else 'N/A'}, " + f"max_audio_chunks={max_audio_chunks}" + ) + + content = [] + + if has_video: + video_content = { + "type": "video", + "video": str(video_path), + "max_frames": actual_max_frames, + "min_frames": min(self.default_min_frames, actual_max_frames), + } + # Optional time-range for segment-level processing + if kwargs.get("video_start") is not None: + video_content["video_start"] = kwargs["video_start"] + if kwargs.get("video_end") is not None: + video_content["video_end"] = kwargs["video_end"] + content.append(video_content) + + if has_audio and audio_path is not None and os.path.exists(audio_path): + try: + import av + + test_container = av.open(audio_path) + if len(test_container.streams.audio) > 0: + audio_content = { + "type": "audio", + "audio": str(audio_path), + } + # Optional time-range for segment-level processing + if kwargs.get("audio_start") is not None: + audio_content["audio_start"] = kwargs["audio_start"] + if kwargs.get("audio_end") is not None: + audio_content["audio_end"] = kwargs["audio_end"] + content.append(audio_content) + else: + logger.info( + f"Audio file {audio_path} has no audio stream, skipping" + ) + has_audio = False + test_container.close() + except Exception as e: + logger.warning( + f"Could not verify audio file {audio_path}: {e}. Skipping audio." + ) + has_audio = False + + content.append({"type": "text", "text": prompt}) + + conversation = [{"role": "user", "content": content}] + + text = self.processor.apply_chat_template( + conversation, tokenize=False, add_generation_prompt=True + ) + + audios, images, videos, mm_metadata = process_mm_info( + conversation, + use_audio_in_video=False, + max_audio_duration=None, + max_audio_chunks=max_audio_chunks, + audio_chunk_duration_sec=kwargs.get("audio_chunk_duration_sec", 10.0), + ) + + if audios is not None: + audios = [a for a in audios if len(a) > 0] + if len(audios) == 0: + audios = None + + if audios is None and "<|audio_pad|>" in text: + text = text.replace("<|audio_pad|>", "").strip() + logger.info("Removed <|audio_pad|> from prompt (no audio available)") + + if max_audio_chunks is not None and audios is not None: + self.stats["audio_chunks_sampled"] += 1 + + self.stats["total_samples"] += 1 + + inputs = {"prompt": text, "multi_modal_data": {}} + + if audios is not None: + inputs["multi_modal_data"]["audio"] = audios + if images is not None: + inputs["multi_modal_data"]["image"] = images + if videos is not None: + inputs["multi_modal_data"]["video"] = videos + + temperature = kwargs.get("temperature", self.temperature) + top_p = kwargs.get("top_p", self.top_p) + top_k = kwargs.get("top_k", self.top_k) + max_tokens = kwargs.get("max_new_tokens", self.max_tokens) + + sampling_params = SamplingParams( + temperature=temperature, + top_p=top_p, + top_k=top_k, + max_tokens=max_tokens, + ) + + logger.info("Generating response...") + + outputs = self.llm.generate([inputs], sampling_params=sampling_params) + response_text = outputs[0].outputs[0].text + + logger.info(f"Generated response ({len(response_text)} chars)") + + return response_text, mm_metadata + + except Exception as e: + error_msg = str(e) + + is_cache_error = ( + "Expected a cached item" in error_msg + or "mm_hash" in error_msg + or "AssertionError" in error_msg + ) + is_engine_dead = ( + "EngineDeadError" in error_msg + or "EngineCore" in error_msg + or "process_input_sockets" in error_msg + ) + is_context_error = any( + keyword in error_msg.lower() + for keyword in [ + "context", + "token", + "length", + "limit", + "maximum", + "exceed", + "longer than", + ] + ) + is_oom = "out of memory" in error_msg.lower() or "OOM" in error_msg + + if is_engine_dead or is_cache_error: + logger.error(f"Engine/Cache error: {e}") + self._reload_engine() + raise RuntimeError(f"Engine/cache error (engine reloaded): {e}") + + if is_context_error: + logger.error(f"Context length error: {e}") + self._reload_engine() + raise RuntimeError(f"Context length exceeded: {e}") + + if is_oom: + logger.error(f"OOM error: {e}") + self.unload() + if torch.cuda.is_available(): + torch.cuda.empty_cache() + self._clear_vllm_cache() + self.load() + raise RuntimeError(f"Out of memory (engine reloaded): {e}") + + logger.error(f"Generation failed: {e}", exc_info=True) + raise RuntimeError(f"Generation failed: {e}") + + def _generate_text_only( + self, prompt: str, kwargs: Dict + ) -> Tuple[str, Dict[str, Any]]: + """Text-only generation for embedded mode (original code, unchanged).""" + from vllm import SamplingParams + + logger.info("Running text-only generation...") + + temperature = kwargs.get("temperature", self.temperature) + top_p = kwargs.get("top_p", self.top_p) + top_k = kwargs.get("top_k", self.top_k) + max_tokens = kwargs.get("max_new_tokens", self.max_tokens) + + sampling_params = SamplingParams( + temperature=temperature, + top_p=top_p, + top_k=top_k, + max_tokens=max_tokens, + ) + + assert self.llm is not None + assert self.processor is not None + + # Wrap in chat template so the model sees role markers + conversation = [{"role": "user", "content": [{"type": "text", "text": prompt}]}] + formatted_prompt = self.processor.apply_chat_template( + conversation, tokenize=False, add_generation_prompt=True + ) + + inputs = {"prompt": formatted_prompt, "multi_modal_data": {}} + + try: + outputs = self.llm.generate([inputs], sampling_params=sampling_params) + response_text: str = outputs[0].outputs[0].text + + logger.info(f"Generated text-only response ({len(response_text)} chars)") + return response_text, {} + + except Exception as e: + logger.error(f"Text-only generation failed: {e}", exc_info=True) + raise RuntimeError(f"Text-only generation failed: {e}") + + # ====================================================================== + # EMBEDDED MODE β€” helper methods (UNCHANGED) + # ====================================================================== + + def _clear_vllm_cache(self): + """Clear vLLM multimodal cache.""" + vllm_cache = Path( + os.environ.get("VLLM_CACHE_ROOT", Path.home() / ".cache/vllm") + ) + mm_cache = vllm_cache / "multimodal_cache" + if mm_cache.exists(): + try: + shutil.rmtree(mm_cache, ignore_errors=True) + logger.debug(f"Cleared multimodal cache: {mm_cache}") + except Exception as e: + logger.warning(f"Failed to clear cache: {e}") + + def _is_engine_alive(self) -> bool: + """Check if vLLM engine is responsive.""" + if self.llm is None: + return False + + from vllm import SamplingParams + + try: + self.llm.generate( + [{"prompt": "test", "multi_modal_data": {}}], + SamplingParams(max_tokens=1), + ) + return True + except Exception: + return False + + def _reload_engine(self): + """Reload vLLM engine after crash.""" + logger.warning("Engine crashed, attempting reload") + + try: + self.unload() + except Exception as e: + logger.warning(f"Error during unload: {e}") + + self._clear_vllm_cache() + time.sleep(15) + + try: + self.load() + logger.info("Engine reloaded successfully") + except Exception as e: + logger.error(f"Failed to reload engine: {e}") + raise RuntimeError(f"Could not recover from engine crash: {e}") + + # ====================================================================== + # INFO / STATS + # ====================================================================== + + def get_model_info(self) -> Dict[str, Any]: + """Get model information and configuration.""" + return { + "model_path": self.model_path, + "model_type": "Thinking" if self.use_thinking else "Instruct", + "backend": "vLLM-server" if self._server_mode else "vLLM-embedded", + "vllm_base_url": self.vllm_base_url if self._server_mode else None, + "native_video": True, + "native_audio": True, + "text_only_support": True, + "tensor_parallel_size": self.tensor_parallel_size, + "gpu_memory_utilization": self.gpu_memory_utilization, + "default_max_frames": self.default_max_frames, + "default_min_frames": self.default_min_frames, + "max_model_len": self.max_model_len, + "audio_feature_rate": self.audio_feature_rate, + "statistics": self.stats, + } + + def get_statistics(self) -> Dict[str, Any]: + """Get model statistics.""" + return self.stats diff --git a/src/sonic_o1_agent/processors/__init__.py b/src/sonic_o1_agent/processors/__init__.py new file mode 100644 index 0000000..abfda39 --- /dev/null +++ b/src/sonic_o1_agent/processors/__init__.py @@ -0,0 +1,9 @@ +"""Processors for multimodal data preparation and prompt building. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from sonic_o1_agent.processors.prompt_builder import PromptBuilder +from sonic_o1_agent.processors.temporal_index import TemporalIndexBuilder + +__all__ = ["PromptBuilder", "TemporalIndexBuilder"] diff --git a/src/sonic_o1_agent/processors/prompt_builder.py b/src/sonic_o1_agent/processors/prompt_builder.py new file mode 100644 index 0000000..e47f943 --- /dev/null +++ b/src/sonic_o1_agent/processors/prompt_builder.py @@ -0,0 +1,223 @@ +"""Dynamic prompt construction for different query types. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +from typing import Dict, Optional + +logger = logging.getLogger(__name__) + + +class PromptBuilder: + """Build contextual prompts based on query type and context.""" + + @staticmethod + def build_prompt( + query: str, + context: Optional[Dict] = None, + query_type: Optional[str] = None, + ) -> str: + """Build a prompt based on query and context. + + Args: + query: User's query + context: Optional context (duration, previous interactions, etc.) + query_type: Optional hint about query type (summarization, qa, etc.) + + Returns: + Formatted prompt string + """ + context = context or {} + + # If query type is explicitly provided, use specialized prompts + if query_type == "summarization": + prompt = PromptBuilder._build_summarization_prompt(query, context) + elif query_type == "qa": + prompt = PromptBuilder._build_qa_prompt(query, context) + elif query_type == "temporal": + prompt = PromptBuilder._build_temporal_prompt(query, context) + else: + prompt = PromptBuilder._build_generic_prompt(query, context) + + # Append temporal grounding so the model cites seconds + prompt = PromptBuilder._append_temporal_grounding(prompt, context) + return prompt + + @staticmethod + def _build_summarization_prompt(query: str, context: Dict) -> str: + """Build prompt for summarization tasks.""" + duration = context.get("duration_seconds") + + base_prompt = "Please analyze this video and provide a comprehensive summary." + + if duration: + base_prompt += f"\n\nThe video is approximately {duration} seconds long." + + if query and query.lower() != "summarize": + base_prompt += f"\n\nSpecific request: {query}" + + base_prompt += ( + "\n\nProvide:\n" + "1. A detailed summary of the key events and content\n" + "2. Important timestamps and their significance\n" + "3. Key points or takeaways" + ) + + return base_prompt + + @staticmethod + def _build_qa_prompt(query: str, context: Dict) -> str: + """Build prompt for question-answering tasks.""" + base_prompt = ( + "Based on the video and audio content, please answer " + "the following question:\n\n" + ) + + base_prompt += f"{query}\n\n" + base_prompt += ( + "Provide a clear and concise answer based on what you observe " + "in the video and hear in the audio." + ) + + return base_prompt + + @staticmethod + def _build_temporal_prompt(query: str, context: Dict) -> str: + """Build prompt for temporal localization tasks.""" + segment_start = context.get("segment_start") + segment_end = context.get("segment_end") + + base_prompt = query + + if segment_start is not None and segment_end is not None: + base_prompt += ( + f"\n\nNote: This segment covers the time range " + f"{segment_start}s to {segment_end}s of the original video." + ) + + base_prompt += ( + "\n\nPlease provide specific timestamps in your answer " + "when referring to events." + ) + + return base_prompt + + @staticmethod + def _build_generic_prompt(query: str, context: Dict) -> str: + """Build generic prompt for any query.""" + previous_context = context.get("previous_interactions") + + base_prompt = query + + if previous_context: + base_prompt = "Continuing our previous conversation:\n\n" + base_prompt + + return base_prompt + + @staticmethod + def _append_temporal_grounding(prompt: str, context: Dict) -> str: + """Prepend temporal grounding directive so the model cites seconds. + + Placed BEFORE the main prompt so the model treats timestamp + citation as a primary requirement, not an afterthought. + """ + parts = [] + + duration = context.get("duration_seconds") + max_frames = context.get("max_frames") + segment_start = context.get("segment_start") + segment_end = context.get("segment_end") + + if max_frames and duration: + interval = round(duration / max(max_frames, 1), 1) + parts.append( + f"The video is {round(duration, 1)}s long. You are " + f"viewing {max_frames} uniformly sampled frames " + f"(one every ~{interval}s). The audio covers the " + f"full duration." + ) + elif max_frames: + parts.append( + f"You are viewing {max_frames} uniformly sampled frames from the video." + ) + + if segment_start is not None and segment_end is not None: + parts.append( + f"This content covers {segment_start}s - " + f"{segment_end}s of the original video." + ) + + if not parts: + return prompt + + temporal_index = context.get("temporal_index") + + # When a temporal index is available, use it as the primary + # grounding source; otherwise fall back to the sampling hint. + if temporal_index: + directive = ( + "IMPORTANT: A timestamped content index of the video " + "is provided below. Use it to cite accurate timestamps " + "in seconds when describing events. Format: " + "'Around 30s, ...' or 'Between 120s and 180s, ...'." + ) + grounding = "\n".join(parts) + return ( + f"{directive}\n\n{grounding}\n\n" + f"--- VIDEO INDEX ---\n{temporal_index}\n" + f"--- END INDEX ---\n\n{prompt}" + ) + + directive = ( + "IMPORTANT: Include approximate timestamps in seconds " + "for key events or points you describe. Use the audio " + "and visual progression to estimate when things occur. " + "Format: 'Around 30s, ...' or 'Between 120s and 180s, " + "...'." + ) + grounding = "\n".join(parts) + return f"{directive}\n\n{grounding}\n\n{prompt}" + + @staticmethod + def detect_query_type(query: str) -> str: + """Detect the type of query based on keywords. + + Args: + query: User's query + + Returns: + Query type: 'summarization', 'qa', 'temporal', or 'generic' + """ + query_lower = query.lower() + + # Temporal first so "what happened at minute 5" is temporal, not summarization + temporal_keywords = [ + "when", + "at what time", + "minute", + "second", + "timestamp", + "between", + ] + if any(kw in query_lower for kw in temporal_keywords): + return "temporal" + + # Summarization keywords + summarization_keywords = [ + "summarize", + "summary", + "overview", + "what happened", + "describe", + "explain the video", + ] + if any(kw in query_lower for kw in summarization_keywords): + return "summarization" + + # Question keywords + qa_keywords = ["what", "who", "where", "why", "how", "which"] + if any(query_lower.startswith(kw) for kw in qa_keywords): + return "qa" + + return "generic" diff --git a/src/sonic_o1_agent/processors/temporal_index.py b/src/sonic_o1_agent/processors/temporal_index.py new file mode 100644 index 0000000..12ab4d3 --- /dev/null +++ b/src/sonic_o1_agent/processors/temporal_index.py @@ -0,0 +1,312 @@ +"""Temporal index builder for real timestamp grounding. + +Splits sampled video frames into segments, generates a short caption +per segment via the VLM, and assembles a timestamped text index that +downstream prompts can reference for accurate temporal citations. + +When the model is running in **server mode** (vLLM HTTP server), +segment captioning is parallelised via ``ThreadPoolExecutor`` so +that vLLM's continuous batching can process them concurrently. +In embedded mode, segments are captioned serially (the in-process +vLLM engine is single-threaded). + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +import math +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Any, Dict, List, Optional, Tuple + +logger = logging.getLogger(__name__) + +# Prompt sent to the model for each segment. +CAPTION_PROMPT = ( + "Briefly describe what is shown and said in this video clip. " + "Be concise (2-3 sentences). Focus on key events, visual " + "content, and any spoken words." +) + + +class TemporalIndexBuilder: + """Build a timestamped text index from video segments. + + The index is a plain-text string of the form:: + + [0s - 90s] Speaker introduces himself and the topic. + [90s - 180s] Diagram of patient history structure shown. + ... + + This string is injected into the main prompt so the model can + cite real timestamps when answering queries. + """ + + def __init__(self, config: Optional[Dict[str, Any]] = None) -> None: + """Initialise the builder. + + All settings are read from ``config`` (typically the + ``temporal_index`` section of ``agent_config.yaml``). + + Args: + config: Configuration dict. Recognised keys: + + - ``min_duration_sec`` -- skip indexing for videos + shorter than this (default ``120``). + - ``num_segments`` -- how many segments to split the + video into (default ``10``). + - ``max_frames_per_segment`` -- frames sampled per + segment caption (default ``16``). + - ``caption_max_tokens`` -- max tokens per caption + (default ``256``). + - ``max_parallel`` -- max concurrent caption requests + in server mode (default ``5``). + """ + config = config or {} + self.min_duration_sec = config.get("min_duration_sec", 120) + self.num_segments = config.get("num_segments", 10) + self.max_frames_per_segment = config.get("max_frames_per_segment", 16) + self.caption_max_tokens = config.get("caption_max_tokens", 256) + self.max_parallel = config.get("max_parallel", 5) + + # ------------------------------------------------------------------ + # Public API + # ------------------------------------------------------------------ + def build_index( + self, + model: Any, + video_path: Optional[str], + audio_path: Optional[str], + duration_sec: float, + ) -> str: + """Build the temporal index for a video. + + Videos shorter than ``min_duration_sec`` are skipped (returns + an empty string) because the model can process the entire clip + in a single pass without needing segment-level grounding. + + Automatically selects parallel or serial captioning based on + whether the model is in server mode. + + Args: + model: A loaded ``Qwen3OmniModel`` (or compatible) instance. + video_path: Path to the video file. + audio_path: Path to the audio file (may be ``None``). + duration_sec: Total video duration in seconds. + + Returns: + Multi-line timestamped index string. Returns an empty + string when the video is ``None``, too short, or + captioning fails entirely. + """ + if not video_path: + return "" + + if duration_sec < self.min_duration_sec: + logger.info( + "Skipping temporal index: video %.1fs < min_duration_sec %ds", + duration_sec, + self.min_duration_sec, + ) + return "" + + segments = self._compute_segments(duration_sec) + logger.info( + "Building temporal index: %d segments over %.1fs video", + len(segments), + duration_sec, + ) + + # Choose parallel vs serial based on model backend + is_server_mode = getattr(model, "_server_mode", False) + + if is_server_mode and len(segments) > 1: + entries = self._caption_segments_parallel( + model, video_path, audio_path, segments + ) + else: + entries = self._caption_segments_serial( + model, video_path, audio_path, segments + ) + + if not entries: + logger.warning("Temporal index is empty -- captioning failed") + return "" + + index_text = "\n".join(entries) + logger.info( + "Temporal index built: %d / %d segments captioned", + len(entries), + len(segments), + ) + return index_text + + # ------------------------------------------------------------------ + # Serial captioning (embedded mode) + # ------------------------------------------------------------------ + def _caption_segments_serial( + self, + model: Any, + video_path: str, + audio_path: Optional[str], + segments: List[Tuple[float, float]], + ) -> List[str]: + """Caption segments one at a time (original behaviour). + + Used in embedded mode where the in-process vLLM engine + processes one request at a time. + """ + logger.info("Temporal indexing: serial mode (%d segments)", len(segments)) + entries: List[str] = [] + + for start_sec, end_sec in segments: + caption = self._caption_segment( + model=model, + video_path=video_path, + audio_path=audio_path, + start_sec=start_sec, + end_sec=end_sec, + max_frames=self.max_frames_per_segment, + ) + if caption: + label = f"[{start_sec:.0f}s - {end_sec:.0f}s]" + entries.append(f"{label} {caption}") + + return entries + + # ------------------------------------------------------------------ + # Parallel captioning (server mode) + # ------------------------------------------------------------------ + def _caption_segments_parallel( + self, + model: Any, + video_path: str, + audio_path: Optional[str], + segments: List[Tuple[float, float]], + ) -> List[str]: + """Caption segments concurrently via ThreadPoolExecutor. + + Each segment fires a separate HTTP request to the vLLM server. + vLLM's continuous batching processes them concurrently, giving + near-linear speedup. + + Results are collected and sorted back to original segment order. + """ + max_workers = min(self.max_parallel, len(segments)) + logger.info( + "Temporal indexing: parallel mode (%d segments, %d workers)", + len(segments), + max_workers, + ) + + # Each future maps to (segment_index, start_sec, end_sec) + results: Dict[int, str] = {} + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + future_to_idx = {} + + for idx, (start_sec, end_sec) in enumerate(segments): + future = executor.submit( + self._caption_segment, + model=model, + video_path=video_path, + audio_path=audio_path, + start_sec=start_sec, + end_sec=end_sec, + max_frames=self.max_frames_per_segment, + ) + future_to_idx[future] = (idx, start_sec, end_sec) + + for future in as_completed(future_to_idx): + idx, start_sec, end_sec = future_to_idx[future] + try: + caption = future.result() + if caption: + label = f"[{start_sec:.0f}s - {end_sec:.0f}s]" + results[idx] = f"{label} {caption}" + logger.debug( + "Segment %d/%d captioned (%.0fs-%.0fs)", + idx + 1, + len(segments), + start_sec, + end_sec, + ) + except Exception as exc: + logger.warning( + "Parallel caption failed for segment %d (%.0fs-%.0fs): %s", + idx, + start_sec, + end_sec, + exc, + ) + + # Return in original segment order + return [results[i] for i in sorted(results.keys())] + + # ------------------------------------------------------------------ + # Internals + # ------------------------------------------------------------------ + def _compute_segments(self, duration_sec: float) -> List[Tuple[float, float]]: + """Divide the video duration into equal-length segments. + + Args: + duration_sec: Total video duration in seconds. + + Returns: + List of ``(start_sec, end_sec)`` tuples. + """ + n = min(self.num_segments, max(1, int(math.ceil(duration_sec / 30)))) + seg_len = duration_sec / n + return [ + (round(i * seg_len, 2), round(min((i + 1) * seg_len, duration_sec), 2)) + for i in range(n) + ] + + def _caption_segment( + self, + model: Any, + video_path: str, + audio_path: Optional[str], + start_sec: float, + end_sec: float, + max_frames: int, + ) -> str: + """Generate a short caption for one video segment. + + Uses the model's ``generate`` method with ``video_start`` / + ``video_end`` kwargs so only the relevant slice is decoded. + + Args: + model: Loaded VLM instance. + video_path: Path to the full video file. + audio_path: Path to audio (may be ``None``). + start_sec: Segment start in seconds. + end_sec: Segment end in seconds. + max_frames: Frames to sample within this segment. + + Returns: + Caption string, or empty string on failure. + """ + try: + # Slice both video AND audio to this segment's time range + # so only the relevant chunk is loaded -- not the full file. + response_text, _ = model.generate( + video_path=video_path, + audio_path=audio_path, + prompt=CAPTION_PROMPT, + max_frames=max_frames, + video_start=start_sec, + video_end=end_sec, + audio_start=start_sec, + audio_end=end_sec, + max_new_tokens=self.caption_max_tokens, + ) + # Collapse to single line for index readability + return " ".join(response_text.strip().split()) + except Exception as exc: + logger.warning( + "Caption failed for segment %.0fs-%.0fs: %s", + start_sec, + end_sec, + exc, + ) + return "" diff --git a/src/sonic_o1_agent/py.typed b/src/sonic_o1_agent/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/src/sonic_o1_agent/utils/__init__.py b/src/sonic_o1_agent/utils/__init__.py new file mode 100644 index 0000000..ea66b6a --- /dev/null +++ b/src/sonic_o1_agent/utils/__init__.py @@ -0,0 +1,8 @@ +"""Utility functions for sonic-o1-agent. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from sonic_o1_agent.utils.segmenter import VideoSegmenter + +__all__ = ["VideoSegmenter"] diff --git a/src/sonic_o1_agent/utils/segmenter.py b/src/sonic_o1_agent/utils/segmenter.py new file mode 100644 index 0000000..2dce2ac --- /dev/null +++ b/src/sonic_o1_agent/utils/segmenter.py @@ -0,0 +1,175 @@ +"""Video and audio segmentation utilities. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +import subprocess +from pathlib import Path + +logger = logging.getLogger(__name__) + + +class VideoSegmenter: + """Utilities for extracting video and audio segments.""" + + def __init__(self): + """Initialize video segmenter.""" + pass + + def extract_video_segment( + self, + input_path: Path, + start_time: float, + end_time: float, + output_path: Path, + ) -> None: + """Extract video segment using ffmpeg. + + Args: + input_path: Path to input video file + start_time: Start time in seconds + end_time: End time in seconds + output_path: Path to output video file + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + + duration = end_time - start_time + + cmd = [ + "ffmpeg", + "-y", + "-ss", + str(start_time), + "-i", + str(input_path), + "-t", + str(duration), + "-c:v", + "libx264", + "-c:a", + "aac", + "-avoid_negative_ts", + "make_zero", + str(output_path), + ] + + try: + subprocess.run( + cmd, + check=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + logger.debug( + f"Extracted video segment: {start_time}s-{end_time}s -> {output_path}" + ) + except subprocess.CalledProcessError as e: + logger.error(f"Failed to extract video segment: {e.stderr.decode()}") + raise + + def extract_audio_segment( + self, + input_path: Path, + start_time: float, + end_time: float, + output_path: Path, + output_format: str = "m4a", + ) -> None: + """Extract audio segment using ffmpeg. + + Args: + input_path: Path to input audio file + start_time: Start time in seconds + end_time: End time in seconds + output_path: Path to output audio file + output_format: Output audio format (m4a, wav, etc.) + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + + duration = end_time - start_time + + if output_format == "wav": + codec = "pcm_s16le" + elif output_format == "m4a": + codec = "aac" + else: + codec = "copy" + + cmd = [ + "ffmpeg", + "-y", + "-ss", + str(start_time), + "-i", + str(input_path), + "-t", + str(duration), + "-c:a", + codec, + "-vn", + str(output_path), + ] + + try: + subprocess.run( + cmd, + check=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + logger.debug( + f"Extracted audio segment: {start_time}s-{end_time}s -> {output_path}" + ) + except subprocess.CalledProcessError as e: + logger.error(f"Failed to extract audio segment: {e.stderr.decode()}") + raise + + def convert_audio_format( + self, + input_path: Path, + output_path: Path, + output_format: str = "wav", + ) -> Path: + """Convert audio file to different format. + + Args: + input_path: Path to input audio file + output_path: Path to output audio file + output_format: Target format (wav, m4a, etc.) + + Returns: + Path to converted audio file + """ + output_path.parent.mkdir(parents=True, exist_ok=True) + + if output_format == "wav": + codec = "pcm_s16le" + elif output_format == "m4a": + codec = "aac" + else: + codec = "copy" + + cmd = [ + "ffmpeg", + "-y", + "-i", + str(input_path), + "-c:a", + codec, + "-vn", + str(output_path), + ] + + try: + subprocess.run( + cmd, + check=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + ) + logger.debug(f"Converted audio: {input_path} -> {output_path}") + return output_path + except subprocess.CalledProcessError as e: + logger.error(f"Failed to convert audio: {e.stderr.decode()}") + raise diff --git a/src/sonic_o1_agent/workflows/__init__.py b/src/sonic_o1_agent/workflows/__init__.py new file mode 100644 index 0000000..433913d --- /dev/null +++ b/src/sonic_o1_agent/workflows/__init__.py @@ -0,0 +1,9 @@ +"""LangGraph workflows for Sonic O1 Agent. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from sonic_o1_agent.workflows.graph import build_sonic_workflow +from sonic_o1_agent.workflows.state import SonicState + +__all__ = ["build_sonic_workflow", "SonicState"] diff --git a/src/sonic_o1_agent/workflows/graph.py b/src/sonic_o1_agent/workflows/graph.py new file mode 100644 index 0000000..bd13321 --- /dev/null +++ b/src/sonic_o1_agent/workflows/graph.py @@ -0,0 +1,146 @@ +"""LangGraph workflow definition. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from langgraph.graph import END, StateGraph + +from sonic_o1_agent.workflows.nodes import SonicNodes +from sonic_o1_agent.workflows.state import SonicState + + +def should_use_reasoning(state: SonicState) -> str: + """Return whether to use CoT reasoning (used when not multi-step). + + Args: + state: Current workflow state. + + Returns: + "reasoning" if use_reasoning is True, else "direct". + """ + if state.get("use_reasoning", False): + return "reasoning" + return "direct" + + +def should_route_inference(state: SonicState) -> str: + """Route to multi-step, reasoning, or direct inference node. + + Args: + state: Current workflow state. + + Returns: + "multi_step", "reasoning", or "direct". + + Raises: + ValueError: If use_multi_step is True but multi_step_plan is missing or empty + (avoids masking planning failures by silently falling back to reasoning/direct). + """ + use_multi = state.get("use_multi_step") + plan = state.get("multi_step_plan") + if use_multi and (not plan or (isinstance(plan, list) and len(plan) == 0)): + raise ValueError( + "use_multi_step is True but multi_step_plan is missing or empty; " + "planning may have failed." + ) + if use_multi and plan: + return "multi_step" + return should_use_reasoning(state) + + +def should_use_reflection(state: SonicState) -> str: + """Return whether to run reflection or go to cleanup. + + Args: + state: Current workflow state. + + Returns: + "reflection" if use_reflection is True, else "cleanup". + """ + if state.get("use_reflection", False): + return "reflection" + return "cleanup" + + +def build_sonic_workflow(config: dict) -> StateGraph: + """Build the Sonic O1 workflow graph. + + Args: + config: Agent configuration dict. + + Returns: + StateGraph (uncompiled) with planning, segmentation, temporal + indexing, inference branches, reflection, and cleanup nodes. + """ + nodes = SonicNodes(config) + + # Create graph + workflow = StateGraph(SonicState) + + # Add nodes + workflow.add_node("planning", nodes.planning_node) + workflow.add_node("segmentation", nodes.segmentation_node) + workflow.add_node("temporal_indexing", nodes.temporal_indexing_node) + workflow.add_node("multi_step", nodes.multi_step_node) + workflow.add_node("reasoning", nodes.reasoning_node) + workflow.add_node("direct", nodes.direct_inference_node) + workflow.add_node("reflection", nodes.reflection_node) + workflow.add_node("cleanup", nodes.cleanup_node) + + # Build workflow + workflow.set_entry_point("planning") + + # Planning β†’ Segmentation (always) + workflow.add_edge("planning", "segmentation") + + # Segmentation β†’ Temporal Indexing (always) + workflow.add_edge("segmentation", "temporal_indexing") + + # Temporal Indexing β†’ Multi-step OR Reasoning OR Direct (conditional) + workflow.add_conditional_edges( + "temporal_indexing", + should_route_inference, + { + "multi_step": "multi_step", + "reasoning": "reasoning", + "direct": "direct", + }, + ) + + # Multi-step β†’ Reflection OR Cleanup (conditional) + workflow.add_conditional_edges( + "multi_step", + should_use_reflection, + { + "reflection": "reflection", + "cleanup": "cleanup", + }, + ) + + # Reasoning β†’ Reflection OR Cleanup (conditional) + workflow.add_conditional_edges( + "reasoning", + should_use_reflection, + { + "reflection": "reflection", + "cleanup": "cleanup", + }, + ) + + # Direct β†’ Reflection OR Cleanup (conditional) + workflow.add_conditional_edges( + "direct", + should_use_reflection, + { + "reflection": "reflection", + "cleanup": "cleanup", + }, + ) + + # Reflection β†’ Cleanup (always) + workflow.add_edge("reflection", "cleanup") + + # Cleanup β†’ END + workflow.add_edge("cleanup", END) + + return workflow diff --git a/src/sonic_o1_agent/workflows/nodes.py b/src/sonic_o1_agent/workflows/nodes.py new file mode 100644 index 0000000..de2b157 --- /dev/null +++ b/src/sonic_o1_agent/workflows/nodes.py @@ -0,0 +1,539 @@ +"""LangGraph nodes for Sonic O1 workflow. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import logging +import tempfile +from pathlib import Path +from typing import Dict, Optional, cast + +from sonic_o1_agent.agents.planner import AgentPlanner +from sonic_o1_agent.agents.planner_advanced import MultiStepPlanner +from sonic_o1_agent.agents.reasoner import ChainOfThoughtReasoner +from sonic_o1_agent.agents.reflection import SelfReflection +from sonic_o1_agent.models.qwen_model import Qwen3OmniModel +from sonic_o1_agent.processors.prompt_builder import PromptBuilder +from sonic_o1_agent.processors.temporal_index import TemporalIndexBuilder +from sonic_o1_agent.utils.segmenter import VideoSegmenter +from sonic_o1_agent.workflows.state import SonicState + +logger = logging.getLogger(__name__) + + +class SonicNodes: + """Container for LangGraph workflow node functions and shared components.""" + + def __init__(self, config: Dict) -> None: + """Initialize node container and lightweight components. + + Args: + config: Agent configuration dict. Model, reasoner, and reflection + are created lazily on first use (load_model). + """ + self.config = config + + # Initialize components (lazy load model) + self.model: Optional[Qwen3OmniModel] = None + self.planner = AgentPlanner() + self.multi_step_planner = MultiStepPlanner(config.get("planning", {})) + self.prompt_builder = PromptBuilder() + self.segmenter = VideoSegmenter() + self.temporal_index_builder = TemporalIndexBuilder( + config.get("temporal_index", {}) + ) + self.reasoner: Optional[ChainOfThoughtReasoner] = None + self.reflection: Optional[SelfReflection] = None + + def load_model(self) -> None: + """Load model, reasoner, and reflection if not already loaded.""" + if self.model is None: + logger.info("Loading model...") + self.model = Qwen3OmniModel(self.config.get("model", {})) + self.model.load() + + self.reasoner = ChainOfThoughtReasoner( + self.model, self.config.get("reasoning", {}) + ) + self.reflection = SelfReflection( + self.model, self.config.get("reflection", {}) + ) + + def planning_node(self, state: SonicState) -> SonicState: + """Plan processing: modalities, segmentation, query type, multi-step. + + Args: + state: Current state with query, video_path, audio_path, flags. + + Returns: + Partial state: plan, should_segment, time_range, query_type, + multi_step_plan (if use_multi_step and query decomposable). + """ + logger.info("Node: Planning") + + _vp = state.get("video_path") + _ap = state.get("audio_path") + video_path = Path(_vp) if _vp else None + audio_path = Path(_ap) if _ap else None + + plan = self.planner.plan_processing( + video_path, + audio_path, + state["query"], + config=self.config.get("processing", {}), + ) + + # Multi-step: decompose query when requested and applicable + multi_step_plan = None + if state.get("use_multi_step") and self.multi_step_planner.should_decompose( + state["query"] + ): + multi_step_plan = self.multi_step_planner.decompose_query(state["query"]) + if multi_step_plan: + logger.info("Multi-step plan: %d steps", len(multi_step_plan)) + else: + multi_step_plan = None + + # If user requested multi-step but we have no plan + # (query not decomposable or + # decomposition failed), + # turn off multi-step so the router falls back to + # reasoning/direct instead of raising. + use_multi_step = bool(state.get("use_multi_step") and multi_step_plan) + + return { + "plan": plan, + "should_segment": plan["should_segment"], + "time_range": plan.get("time_range"), + "query_type": plan["query_type"], + "multi_step_plan": multi_step_plan, + "use_multi_step": use_multi_step, + } + + def segmentation_node(self, state: SonicState) -> SonicState: + """Segment video/audio to time range when planning requested it. + + Args: + state: Must contain should_segment, time_range, video_path, + audio_path. + + Returns: + Partial state: actual_video_path, actual_audio_path, temp_files. + """ + logger.info("Node: Segmentation") + + if not state["should_segment"] or not state.get("time_range"): + # No segmentation needed + return { + "actual_video_path": state.get("video_path"), + "actual_audio_path": state.get("audio_path"), + "temp_files": [], + } + + # Segment + _vp2 = state.get("video_path") + _ap2 = state.get("audio_path") + video_path = Path(_vp2) if _vp2 else None + audio_path = Path(_ap2) if _ap2 else None + time_range_seg = state["time_range"] + assert time_range_seg is not None + start_time, end_time = time_range_seg + + temp_files = [] + temp_video = None + temp_audio = None + + if video_path and video_path.exists(): + with tempfile.NamedTemporaryFile( + suffix=".mp4", prefix="sonic_seg_", delete=False + ) as f: + temp_video = Path(f.name) + self.segmenter.extract_video_segment( + video_path, start_time, end_time, temp_video + ) + temp_files.append(str(temp_video)) + + if audio_path and audio_path.exists(): + audio_format = audio_path.suffix[1:] + with tempfile.NamedTemporaryFile( + suffix=f".{audio_format}", prefix="sonic_seg_", delete=False + ) as f: + temp_audio = Path(f.name) + self.segmenter.extract_audio_segment( + audio_path, start_time, end_time, temp_audio, audio_format + ) + temp_files.append(str(temp_audio)) + + return { + "actual_video_path": str(temp_video) + if temp_video + else state.get("video_path"), + "actual_audio_path": str(temp_audio) + if temp_audio + else state.get("audio_path"), + "temp_files": temp_files, + } + + def temporal_indexing_node(self, state: SonicState) -> SonicState: + """Build a timestamped caption index of the video for grounding. + + Splits the video into segments, generates a short VLM caption per + segment, and assembles a text index injected into downstream prompts. + + Args: + state: Must contain plan (duration_seconds), actual_video_path. + + Returns: + Partial state: temporal_index (str or None). + """ + logger.info("Node: Temporal Indexing") + + duration = state["plan"].get("duration_seconds") + video_path = state.get("actual_video_path") + + if not video_path or not duration: + return {"temporal_index": None} + + self.load_model() + assert self.model is not None + + index_text = self.temporal_index_builder.build_index( + model=self.model, + video_path=video_path, + audio_path=state.get("actual_audio_path"), + duration_sec=duration, + ) + + return {"temporal_index": index_text or None} + + def reasoning_node(self, state: SonicState) -> SonicState: + """Run Chain-of-Thought reasoning over video/audio and query. + + Args: + state: Query, paths, plan, temporal_index, time_range, etc. + + Returns: + Partial state: response, reasoning_chain, reasoning_confidence, + reasoning_mode, reasoning_trace. + """ + logger.info("Node: Reasoning") + + self.load_model() + assert self.reasoner is not None + + context = { + "max_frames": state.get("max_frames") or state["plan"]["max_frames"], + "max_audio_chunks": state.get("max_audio_chunks") + or state["plan"].get("max_audio_chunks"), + "query_type": state["query_type"], + "duration_seconds": state["plan"].get("duration_seconds"), + "temporal_index": state.get("temporal_index"), + } + + time_range_r = state.get("time_range") + if time_range_r: + context["segment_start"] = time_range_r[0] + context["segment_end"] = time_range_r[1] + + reasoning_result = self.reasoner.reason( + query=state["query"], + video_path=state.get("actual_video_path"), + audio_path=state.get("actual_audio_path"), + context=context, + ) + + reasoning_trace = self.reasoner.get_reasoning_trace(reasoning_result) + + return { + "response": reasoning_result["final_answer"], + "reasoning_chain": reasoning_result["reasoning_chain"], + "reasoning_confidence": reasoning_result["confidence"], + "reasoning_mode": "chain_of_thought", + "reasoning_trace": reasoning_trace, + } + + def multi_step_node(self, state: SonicState) -> SonicState: + """Execute multi-step plan via executor_fn (direct inference per step). + + Args: + state: Must contain multi_step_plan, actual paths, plan, etc. + + Returns: + Partial state: response, reasoning_mode "multi_step", + steps_executed. Falls back to direct_inference_node if no plan. + """ + logger.info("Node: Multi-Step Execution") + + self.load_model() + assert self.model is not None + model = self.model + + multi_step_plan = state["multi_step_plan"] + if not multi_step_plan: + logger.info( + "multi_step_plan empty or missing; falling back to single direct inference" + ) + return self.direct_inference_node(state) + + max_frames = state.get("max_frames") or state["plan"]["max_frames"] + max_audio_chunks = state.get("max_audio_chunks") or state["plan"].get( + "max_audio_chunks" + ) + video_path = state.get("actual_video_path") + audio_path = state.get("actual_audio_path") + base_context = { + "query_type": state["query_type"], + "max_frames": max_frames, + "duration_seconds": state["plan"].get("duration_seconds"), + "temporal_index": state.get("temporal_index"), + } + time_range_ms = state.get("time_range") + if time_range_ms: + base_context["segment_start"] = time_range_ms[0] + base_context["segment_end"] = time_range_ms[1] + + def executor_fn( + query: str, + video_path_arg=None, + audio_path_arg=None, + context=None, + ): + context = context or {} + ctx = {**base_context} + if context: + ctx["previous_steps"] = "\n".join( + f"Step {k}: {v}" + for k, v in sorted(context.items()) + if isinstance(v, str) + ) + prompt = self.prompt_builder.build_prompt( + query, + context=ctx, + query_type=state["query_type"], + ) + if ctx.get("previous_steps"): + prompt = ( + "Context from previous steps:\n" + f"{ctx['previous_steps']}\n\n" + f"Current sub-task: {prompt}" + ) + response_text, _ = model.generate( + video_path=video_path_arg or video_path, + audio_path=audio_path_arg or audio_path, + prompt=prompt, + max_frames=max_frames, + max_audio_chunks=max_audio_chunks, + ) + return response_text + + result = self.multi_step_planner.execute_plan( + multi_step_plan, + executor_fn, + video_path=video_path, + audio_path=audio_path, + ) + + return { + "response": result["final_answer"], + "reasoning_mode": "multi_step", + "steps_executed": result["steps_executed"], + } + + def direct_inference_node(self, state: SonicState) -> SonicState: + """Run single-pass inference with temporal grounding and evidence. + + Args: + state: Query, actual paths, plan, temporal_index, time_range. + + Returns: + Partial state: response, reasoning_mode "direct", evidence. + """ + logger.info("Node: Direct Inference") + + self.load_model() + assert self.model is not None + + max_frames = state.get("max_frames") or state["plan"]["max_frames"] + max_audio_chunks = state.get("max_audio_chunks") or state["plan"].get( + "max_audio_chunks" + ) + + # Build prompt with temporal grounding context + context = { + "query_type": state["query_type"], + "max_frames": max_frames, + "duration_seconds": state["plan"].get("duration_seconds"), + "temporal_index": state.get("temporal_index"), + } + time_range_di = state.get("time_range") + if time_range_di: + context["segment_start"] = time_range_di[0] + context["segment_end"] = time_range_di[1] + + prompt = self.prompt_builder.build_prompt( + state["query"], + context=context, + query_type=state["query_type"], + ) + + # Generate + response, metadata = self.model.generate( + video_path=state.get("actual_video_path"), + audio_path=state.get("actual_audio_path"), + prompt=prompt, + max_frames=max_frames, + max_audio_chunks=max_audio_chunks, + ) + + # Compact evidence summary + evidence = self._format_evidence(metadata) + + return { + "response": response, + "reasoning_mode": "direct", + "evidence": evidence, + } + + def _format_evidence(self, metadata: dict) -> dict: + """Format model metadata into a compact evidence summary. + + Returns only aggregate stats (duration, counts, coverage) in + seconds; no per-frame or per-chunk indices. + + Args: + metadata: Dict with optional video_metadata and audio_metadata + lists (from model.generate). + + Returns: + Dict with optional "video" and "audio" keys and aggregate stats. + """ + evidence = {} + + if metadata.get("video_metadata"): + vm = metadata["video_metadata"][0] + evidence["video"] = { + "duration_sec": vm["duration_sec"], + "frames_analyzed": vm["frames_sampled"], + "sampling_interval_sec": vm["sampling_interval_sec"], + "coverage_sec": vm["coverage_sec"], + } + + if metadata.get("audio_metadata"): + am = metadata["audio_metadata"][0] + evidence["audio"] = { + "duration_sec": am["duration_sec"], + "chunks_analyzed": am["chunks_analyzed"], + "chunk_duration_sec": am["chunk_duration_sec"], + "coverage_sec": am["coverage_sec"], + } + + return evidence + + def reflection_node(self, state: SonicState) -> SonicState: + """Evaluate and optionally refine response; optional hallucination check. + + Uses single evaluate+refine or iterative_refinement and optionally + detect_hallucination based on config (reflection section). + + Args: + state: Must contain query, response (from inference node). + + Returns: + Partial state: response (possibly refined), reflection, + was_refined, refinement_history, hallucination_assessment. + """ + logger.info("Node: Reflection") + + self.load_model() + assert self.reflection is not None + + ref_config = self.config.get("reflection", {}) + use_iterative = ref_config.get("use_iterative_refinement", False) + check_hallucination = ref_config.get("check_hallucination", False) + + video_path = state.get("actual_video_path") + audio_path = state.get("actual_audio_path") + max_frames = state.get("max_frames") or state["plan"]["max_frames"] + + if use_iterative: + # Iterative refinement until ACCEPT, REJECT, or confidence threshold + iter_result = self.reflection.iterative_refinement( + state["query"], + state["response"], + video_path=video_path, + audio_path=audio_path, + max_frames=max_frames, + ) + result = { + "response": iter_result["final_response"], + "reflection": { + "final_confidence": iter_result["final_confidence"], + "refinement_history": iter_result["refinement_history"], + "total_attempts": iter_result["total_attempts"], + }, + "refinement_history": iter_result["refinement_history"], + "was_refined": iter_result["total_attempts"] > 1, + } + if iter_result["refinement_history"]: + result["original_response"] = iter_result["refinement_history"][0].get( + "response", state["response"] + ) + else: + # Single evaluate + optional one-shot refine + reflection_result = self.reflection.evaluate_response( + state["query"], state["response"] + ) + result = { + "reflection": { + "confidence": reflection_result["confidence"], + "scores": reflection_result["scores"], + "strengths": reflection_result["strengths"], + "weaknesses": reflection_result["weaknesses"], + }, + } + if reflection_result["recommendation"] == "REFINE": + logger.info("Refining response...") + refined = self.reflection.refine_response( + state["query"], + state["response"], + reflection_result, + video_path, + audio_path, + max_frames, + ) + result["original_response"] = state["response"] + result["response"] = refined + result["was_refined"] = True + else: + result["was_refined"] = False + + # Optional hallucination check on final response + if check_hallucination: + response_to_check = result.get("response", state["response"]) + halluc = self.reflection.detect_hallucination( + response_to_check, + video_path=video_path, + audio_path=audio_path, + ) + result["hallucination_assessment"] = halluc + + return cast(SonicState, result) + + def cleanup_node(self, state: SonicState) -> SonicState: + """Remove temporary files created by segmentation. + + Args: + state: May contain temp_files list. + + Returns: + Empty dict (no state update). + """ + logger.info("Node: Cleanup") + + for temp_file in state.get("temp_files", []): + temp_path = Path(temp_file) + if temp_path.exists(): + temp_path.unlink() + logger.debug("Cleaned up: %s", temp_file) + + return {} diff --git a/src/sonic_o1_agent/workflows/state.py b/src/sonic_o1_agent/workflows/state.py new file mode 100644 index 0000000..c33accf --- /dev/null +++ b/src/sonic_o1_agent/workflows/state.py @@ -0,0 +1,65 @@ +"""State definition for LangGraph workflow. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from typing import Any, Dict, List, Optional, Tuple, TypedDict + + +class SonicState(TypedDict, total=False): + """State that flows through the LangGraph workflow. + + All keys are optional (total=False). Nodes read and write partial + updates; the graph merges them. + """ + + # Input + query: str + video_path: Optional[str] + audio_path: Optional[str] + max_frames: Optional[int] + max_audio_chunks: Optional[int] + + # Processing flags + use_reasoning: bool + use_reflection: bool + use_multi_step: bool + + # Planning outputs + plan: Dict[str, Any] + should_segment: bool + time_range: Optional[Tuple[float, float]] + query_type: str + + # Segmentation outputs + actual_video_path: Optional[str] + actual_audio_path: Optional[str] + temp_files: List[str] + + # Temporal index (frame-captioned segment descriptions) + temporal_index: Optional[str] + + # Reasoning outputs + reasoning_chain: Optional[List[Dict]] + reasoning_confidence: Optional[float] + reasoning_trace: Optional[str] + + # Reflection outputs + reflection: Optional[Dict] + was_refined: bool + original_response: Optional[str] + refinement_history: Optional[List[Dict]] + hallucination_assessment: Optional[Dict] + + # Multi-step outputs + multi_step_plan: Optional[List[Dict]] + steps_executed: Optional[Dict] + + # Final output + response: str + reasoning_mode: str + modalities_used: Dict[str, bool] + evidence: Optional[Dict[str, Any]] + + # Metadata + error: Optional[str] diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..fadcd6f --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,4 @@ +"""Unit tests for sonic-o1-agent components. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..6c53b4d --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,30 @@ +"""Pytest configuration and shared fixtures. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import pytest + + +@pytest.fixture +def minimal_agent_config() -> dict: + """Minimal agent config sufficient for workflow init and graph tests. + + Returns: + Dict with model and processing keys; no real model is loaded + when only graph structure or validation is tested. + """ + return { + "model": {"model_path": "test/model"}, + "processing": {}, + } + + +@pytest.fixture +def my_test_number() -> int: + """Sample numeric fixture for placeholder tests. + + Returns: + int: A test number (42). + """ + return 42 diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py new file mode 100644 index 0000000..c6e46f0 --- /dev/null +++ b/tests/integration/__init__.py @@ -0,0 +1,4 @@ +"""Integration tests for sonic-o1-agent. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" diff --git a/tests/integration/test_agent_workflow.py b/tests/integration/test_agent_workflow.py new file mode 100644 index 0000000..9af7970 --- /dev/null +++ b/tests/integration/test_agent_workflow.py @@ -0,0 +1,280 @@ +"""Integration tests for agent workflow with LangGraph. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import pytest + +from sonic_o1_agent import SonicAgent + + +class TestAgentInitialization: + """Test agent initialization and configuration.""" + + def test_agent_init_minimal_config(self, minimal_agent_config): + """Agent initializes with minimal config and has app and config.""" + agent = SonicAgent(minimal_agent_config) + + assert agent is not None + assert hasattr(agent, "app") + assert hasattr(agent, "config") + assert agent.config == minimal_agent_config + + def test_agent_init_full_config(self): + """Test agent with complete config.""" + config = { + "model": { + "model_path": "Qwen/Qwen3-Omni-0.5B", + "tensor_parallel_size": 1, + "max_frames": 128, + }, + "processing": { + "max_frames": 256, + "max_audio_chunks": 5, + }, + "reasoning": { + "max_reasoning_steps": 5, + "enable_verification": True, + }, + "reflection": { + "confidence_threshold": 0.7, + "max_refinement_attempts": 2, + }, + } + agent = SonicAgent(config) + + assert agent.config == config + assert agent.config["model"]["max_frames"] == 128 + assert agent.config["processing"]["max_audio_chunks"] == 5 + + +class TestLangGraphWorkflow: + """Test LangGraph workflow structure.""" + + def test_workflow_compilation(self, minimal_agent_config): + """Workflow compiles and app is set.""" + agent = SonicAgent(minimal_agent_config) + + assert hasattr(agent, "app") + assert agent.app is not None + + def test_workflow_has_nodes(self, minimal_agent_config): + """Workflow graph includes all expected nodes.""" + agent = SonicAgent(minimal_agent_config) + + graph = agent.app.get_graph() + node_names = [node.id for node in graph.nodes.values()] + + # Check for expected nodes (including temporal_indexing and multi_step) + expected_nodes = [ + "planning", + "segmentation", + "temporal_indexing", + "multi_step", + "reasoning", + "direct", + "reflection", + "cleanup", + ] + + for node in expected_nodes: + assert node in node_names, f"Missing node: {node}" + + +class TestInputValidation: + """Test input validation.""" + + def test_process_requires_query(self, minimal_agent_config): + """process raises ValueError when query is empty.""" + agent = SonicAgent(minimal_agent_config) + + with pytest.raises(ValueError, match="Query cannot be empty"): + agent.process(video_path="test.mp4", query="") + + def test_process_requires_media(self, minimal_agent_config): + """process raises ValueError when neither video nor audio given.""" + agent = SonicAgent(minimal_agent_config) + + with pytest.raises( + ValueError, + match="At least one of video_path or audio_path required", + ): + agent.process(query="test query") + + +class TestStateManagement: + """Test LangGraph state management.""" + + def test_initial_state_preparation(self): + """Test initial state is prepared correctly.""" + # This tests the state preparation logic + initial_state = { + "query": "test query", + "video_path": "test.mp4", + "audio_path": "test.m4a", + "max_frames": 128, + "use_reasoning": True, + "use_reflection": False, + "temp_files": [], + } + + # Verify required fields + assert "query" in initial_state + assert "video_path" in initial_state + assert "use_reasoning" in initial_state + assert "temp_files" in initial_state + assert "use_multi_step" not in initial_state or isinstance( + initial_state.get("use_multi_step"), bool + ) + + +class TestWorkflowRoutingMultiStep: + """Test multi-step routing (should_route_inference).""" + + def test_route_inference_multi_step_when_planned(self): + """When use_multi_step and multi_step_plan set, route to multi_step.""" + from sonic_o1_agent.workflows.graph import should_route_inference + + state = { + "use_multi_step": True, + "multi_step_plan": [{"step_id": 1, "query": "x"}], + } + assert should_route_inference(state) == "multi_step" + + def test_route_inference_reasoning_when_no_multi_step(self): + """When use_reasoning True and no multi_step, route to reasoning.""" + from sonic_o1_agent.workflows.graph import should_route_inference + + state = {"use_reasoning": True} + assert should_route_inference(state) == "reasoning" + + def test_route_inference_direct_when_no_flags(self): + """When no multi_step or reasoning, route to direct.""" + from sonic_o1_agent.workflows.graph import should_route_inference + + state = {} + assert should_route_inference(state) == "direct" + + def test_route_inference_multi_step_empty_plan_raises(self): + """When use_multi_step is True but multi_step_plan missing/empty, raise to avoid masking planning failures.""" + from sonic_o1_agent.workflows.graph import should_route_inference + + for plan_value in (None, [], []): + state = {"use_multi_step": True, "multi_step_plan": plan_value} + with pytest.raises(ValueError, match="multi_step_plan is missing or empty"): + should_route_inference(state) + + def test_route_inference_without_multi_step_uses_reasoning_or_direct(self): + """When use_multi_step is False, route to reasoning or direct.""" + from sonic_o1_agent.workflows.graph import should_route_inference + + state = {"use_multi_step": False} + assert should_route_inference(state) == "direct" + state["use_reasoning"] = True + assert should_route_inference(state) == "reasoning" + + +class TestProcessStream: + """Test process_stream API (streaming workflow events).""" + + def test_process_stream_requires_query(self, minimal_agent_config): + """process_stream raises ValueError when query is empty.""" + agent = SonicAgent(minimal_agent_config) + + with pytest.raises(ValueError, match="Query cannot be empty"): + next(agent.process_stream(video_path="x.mp4", query="")) + + def test_process_stream_requires_media(self, minimal_agent_config): + """process_stream raises ValueError when no video or audio.""" + agent = SonicAgent(minimal_agent_config) + + with pytest.raises( + ValueError, + match="At least one of video_path or audio_path required", + ): + next(agent.process_stream(query="test")) + + def test_process_stream_yields_node_and_state(self, minimal_agent_config): + """process_stream yields events with node and state; first is planning.""" + agent = SonicAgent(minimal_agent_config) + + stream = agent.process_stream( + video_path="nonexistent.mp4", + query="summarize", + ) + assert hasattr(stream, "__iter__") + assert hasattr(stream, "__next__") + + event = next(stream) + assert "node" in event + assert "state" in event + assert event["node"] == "planning" + assert isinstance(event["state"], dict) + assert "plan" in event["state"] or "query_type" in event["state"] + + +class TestContextManager: + """Test agent context manager usage.""" + + def test_context_manager_structure(self, minimal_agent_config): + """Agent implements __enter__ and __exit__.""" + agent = SonicAgent(minimal_agent_config) + + assert hasattr(agent, "__enter__") + assert hasattr(agent, "__exit__") + + def test_context_manager_usage(self, minimal_agent_config): + """Agent can be used as context manager and exposes app.""" + with SonicAgent(minimal_agent_config) as agent: + assert agent is not None + assert hasattr(agent, "app") + + +class TestWorkflowConditionals: + """Test workflow conditional routing (reasoning and reflection).""" + + def test_reasoning_flag_routing(self): + """should_use_reasoning returns reasoning or direct by flag.""" + from sonic_o1_agent.workflows.graph import should_use_reasoning + + assert should_use_reasoning({"use_reasoning": True}) == "reasoning" + assert should_use_reasoning({"use_reasoning": False}) == "direct" + + def test_reflection_flag_routing(self): + """should_use_reflection returns reflection or cleanup by flag.""" + from sonic_o1_agent.workflows.graph import should_use_reflection + + assert should_use_reflection({"use_reflection": True}) == "reflection" + assert should_use_reflection({"use_reflection": False}) == "cleanup" + + +class TestModelInfo: + """Test model info retrieval.""" + + def test_get_model_info(self, minimal_agent_config): + """get_model_info returns backend and workflow keys.""" + agent = SonicAgent(minimal_agent_config) + + info = agent.get_model_info() + + assert "backend" in info + assert "workflow" in info + assert info["backend"] == "LangGraph + vLLM" + assert info["workflow"] == "enabled" + + +@pytest.mark.skip(reason="Requires actual model and media files") +class TestEndToEndWorkflow: + """End-to-end workflow tests (requires model).""" + + def test_full_workflow_direct(self): + """Test full workflow with direct inference.""" + pass + + def test_full_workflow_reasoning(self): + """Test full workflow with CoT reasoning.""" + pass + + def test_full_workflow_reflection(self): + """Test full workflow with reflection.""" + pass diff --git a/tests/unit/test_multimodal_engine.py b/tests/unit/test_multimodal_engine.py new file mode 100644 index 0000000..57eb2a1 --- /dev/null +++ b/tests/unit/test_multimodal_engine.py @@ -0,0 +1,147 @@ +"""Unit tests for multimodal processing engine. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +import numpy as np + +from sonic_o1_agent.core.multimodal_engine import ( + ceil_by_factor, + floor_by_factor, + round_by_factor, + smart_nframes, + smart_resize, +) + + +class TestMathHelpers: + """Test mathematical helper functions.""" + + def test_round_by_factor(self): + """Test rounding to nearest factor (Python round: half to even).""" + # 25/10 = 2.5 -> round(2.5) = 2 in Python 3 (banker's rounding) + assert round_by_factor(25, 10) == 20 + assert round_by_factor(24, 10) == 20 + assert round_by_factor(26, 10) == 30 + assert round_by_factor(28, 28) == 28 + assert round_by_factor(30, 28) == 28 + + def test_ceil_by_factor(self): + """Test ceiling division by factor.""" + assert ceil_by_factor(25, 10) == 30 + assert ceil_by_factor(20, 10) == 20 + assert ceil_by_factor(21, 10) == 30 + assert ceil_by_factor(1, 28) == 28 + + def test_floor_by_factor(self): + """Test floor division by factor.""" + assert floor_by_factor(25, 10) == 20 + assert floor_by_factor(29, 10) == 20 + assert floor_by_factor(30, 10) == 30 + assert floor_by_factor(27, 28) == 0 + + +class TestSmartResize: + """Test smart resize for images/videos.""" + + def test_resize_maintains_aspect_ratio(self): + """Test that resize maintains aspect ratio.""" + h, w = smart_resize(1080, 1920, factor=28) + aspect_original = 1920 / 1080 + aspect_resized = w / h + assert abs(aspect_original - aspect_resized) < 0.1 + + def test_resize_divisible_by_factor(self): + """Test output is divisible by factor.""" + h, w = smart_resize(1080, 1920, factor=28) + assert h % 28 == 0 + assert w % 28 == 0 + + def test_resize_respects_min_pixels(self): + """Test minimum pixel constraint.""" + min_pixels = 4 * 28 * 28 + h, w = smart_resize(100, 100, factor=28, min_pixels=min_pixels) + assert h * w >= min_pixels + + def test_resize_respects_max_pixels(self): + """Test maximum pixel constraint.""" + max_pixels = 1000 * 28 * 28 + h, w = smart_resize(4000, 4000, factor=28, max_pixels=max_pixels) + assert h * w <= max_pixels + + def test_resize_caching(self): + """Test that results are cached.""" + # Call twice with same params + result1 = smart_resize(1080, 1920, factor=28) + result2 = smart_resize(1080, 1920, factor=28) + assert result1 == result2 + assert result1 is result2 # Same object (cached) + + +class TestSmartNFrames: + """Test smart frame count calculation.""" + + def test_nframes_with_explicit_value(self): + """Test when nframes is explicitly provided.""" + ele = {"nframes": 100} + result = smart_nframes(ele, total_frames=1000, video_fps=30.0) + assert result == 100 + + def test_nframes_respects_min_frames(self): + """Test minimum frames constraint.""" + ele = {"fps": 0.1, "min_frames": 64} + result = smart_nframes(ele, total_frames=1000, video_fps=30.0) + assert result >= 64 + + def test_nframes_respects_max_frames(self): + """Test maximum frames constraint.""" + ele = {"fps": 10.0, "max_frames": 128} + result = smart_nframes(ele, total_frames=1000, video_fps=30.0) + assert result <= 128 + + def test_nframes_divisible_by_frame_factor(self): + """Test output divisible by FRAME_FACTOR (2).""" + ele = {"fps": 1.0} + result = smart_nframes(ele, total_frames=1000, video_fps=30.0) + assert result % 2 == 0 + + def test_nframes_not_exceed_total(self): + """Test doesn't exceed total available frames.""" + ele = {"fps": 10.0} + result = smart_nframes(ele, total_frames=100, video_fps=30.0) + assert result <= 100 + + def test_nframes_fps_calculation(self): + """Test FPS-based sampling calculation.""" + ele = {"fps": 1.0} # 1 frame per second + # 1000 frames at 30fps = 33.33 seconds + # At 1fps sampling = ~33 frames + result = smart_nframes(ele, total_frames=1000, video_fps=30.0) + expected = int(1000 * 1.0 / 30.0) + # Should be close (accounting for factor rounding) + assert abs(result - expected) < 10 + + +class TestAudioProcessing: + """Test audio-related utilities.""" + + def test_sample_rate_constant(self): + """Test SAMPLE_RATE is defined correctly.""" + from sonic_o1_agent.core.multimodal_engine import SAMPLE_RATE + + assert SAMPLE_RATE == 16000 + + def test_audio_chunking_logic(self): + """Test audio chunking calculation.""" + # Simulate 60 seconds of audio at 16kHz + total_samples = 60 * 16000 + chunk_duration = 10.0 # 10 second chunks + max_chunks = 3 + + samples_per_chunk = int(chunk_duration * 16000) + num_chunks = int(np.ceil(total_samples / samples_per_chunk)) + + assert num_chunks == 6 # 60s / 10s = 6 chunks + + # Should trigger sampling since 6 > 3 + assert num_chunks > max_chunks diff --git a/tests/unit/test_planner.py b/tests/unit/test_planner.py new file mode 100644 index 0000000..7acbf9e --- /dev/null +++ b/tests/unit/test_planner.py @@ -0,0 +1,115 @@ +"""Unit tests for AgentPlanner. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from pathlib import Path + +from sonic_o1_agent.agents.planner import AgentPlanner + + +class TestTimeRangeParsing: + """Test time range parsing from queries.""" + + def test_parse_minute_to_minute(self): + """Test 'minute X to Y' pattern.""" + query = "what happened from minute 5 to 10" + result = AgentPlanner.parse_time_range(query) + assert result == (300, 600) + + def test_parse_between_minutes(self): + """Test 'between minute X and Y' pattern.""" + query = "what happened between minute 2 and 3" + result = AgentPlanner.parse_time_range(query) + assert result == (120, 180) + + def test_parse_at_minute(self): + """Test 'at minute X' pattern with window.""" + query = "what happened at minute 5" + result = AgentPlanner.parse_time_range(query) + assert result == (270, 330) # 5*60 Β± 30 seconds + + def test_parse_around_minute(self): + """Test 'around minute X' pattern.""" + query = "around minute 10" + result = AgentPlanner.parse_time_range(query) + assert result == (570, 630) + + def test_parse_first_minutes(self): + """Test 'first X minutes' pattern.""" + query = "summarize the first 10 minutes" + result = AgentPlanner.parse_time_range(query) + assert result == (0, 600) + + def test_no_time_range(self): + """Test query without time range.""" + query = "summarize this video" + result = AgentPlanner.parse_time_range(query) + assert result is None + + +class TestModalityDetection: + """Test modality detection.""" + + def test_both_modalities(self): + """Test with both video and audio.""" + video = Path(__file__) # Use test file as dummy + audio = Path(__file__) + result = AgentPlanner.determine_modalities(video, audio) + assert result["has_video"] is True + assert result["has_audio"] is True + + def test_video_only(self): + """Test with video only.""" + video = Path(__file__) + result = AgentPlanner.determine_modalities(video, None) + assert result["has_video"] is True + assert result["has_audio"] is False + + def test_audio_only(self): + """Test with audio only.""" + audio = Path(__file__) + result = AgentPlanner.determine_modalities(None, audio) + assert result["has_video"] is False + assert result["has_audio"] is True + + def test_no_modalities(self): + """Test with neither.""" + result = AgentPlanner.determine_modalities(None, None) + assert result["has_video"] is False + assert result["has_audio"] is False + + def test_nonexistent_files(self): + """Test with paths that don't exist.""" + video = Path("/nonexistent/video.mp4") + audio = Path("/nonexistent/audio.m4a") + result = AgentPlanner.determine_modalities(video, audio) + assert result["has_video"] is False + assert result["has_audio"] is False + + +class TestProcessingPlan: + """Test complete processing plan generation.""" + + def test_plan_basic_query(self): + """Test plan for basic query.""" + plan = AgentPlanner.plan_processing( + video_path=None, + audio_path=None, + query="summarize this", + config={"max_frames": 256}, + ) + assert "modalities" in plan + assert "query_type" in plan + assert plan["should_segment"] is False + + def test_plan_includes_config(self): + """Test that config values are included.""" + config = {"max_frames": 128, "max_audio_chunks": 5} + plan = AgentPlanner.plan_processing( + video_path=None, + audio_path=None, + query="test", + config=config, + ) + assert plan["max_frames"] == 128 diff --git a/tests/unit/test_planner_advanced.py b/tests/unit/test_planner_advanced.py new file mode 100644 index 0000000..e2f082d --- /dev/null +++ b/tests/unit/test_planner_advanced.py @@ -0,0 +1,90 @@ +"""Unit tests for MultiStepPlanner. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from sonic_o1_agent.agents.planner_advanced import MultiStepPlanner + + +class TestShouldDecompose: + """Test should_decompose keyword detection.""" + + def test_compare_triggers_decompose(self): + """Queries with 'compare' should be decomposed.""" + planner = MultiStepPlanner() + assert planner.should_decompose("Compare the two approaches") is True + + def test_contrast_triggers_decompose(self): + """Queries with 'contrast' should be decomposed.""" + planner = MultiStepPlanner() + assert planner.should_decompose("Contrast A and B") is True + + def test_both_triggers_decompose(self): + """Queries with 'both' should be decomposed.""" + planner = MultiStepPlanner() + assert planner.should_decompose("Analyze both speakers") is True + + def test_simple_query_no_decompose(self): + """Simple summarization query should not be decomposed.""" + planner = MultiStepPlanner() + assert planner.should_decompose("Summarize this video") is False + + def test_why_triggers_decompose(self): + """Queries with 'explain why' should be decomposed.""" + planner = MultiStepPlanner() + assert planner.should_decompose("Explain why this happened") is True + + +class TestDecomposeQuery: + """Test decompose_query returns step list.""" + + def test_comparison_returns_multiple_steps(self): + """Comparison query returns multiple steps.""" + planner = MultiStepPlanner() + steps = planner.decompose_query("Compare defense vs prosecutor") + assert len(steps) >= 2 + assert all("step_id" in s and "query" in s for s in steps) + + def test_default_returns_single_step(self): + """Query without complex keywords returns single comprehensive step.""" + planner = MultiStepPlanner() + steps = planner.decompose_query("Summarize the content") + assert len(steps) == 1 + assert steps[0]["step_id"] == 1 + assert steps[0]["action"] == "analyze" + + def test_max_steps_limits_result(self): + """max_steps in config caps number of steps returned.""" + planner = MultiStepPlanner(config={"max_steps": 2}) + steps = planner.decompose_query("Compare A and B") + assert len(steps) <= 2 + + def test_step_has_required_keys(self): + """Each step has step_id, action, description, query.""" + planner = MultiStepPlanner() + steps = planner.decompose_query("What happened first, then after?") + for step in steps: + assert "step_id" in step + assert "action" in step + assert "description" in step + assert "query" in step + + +class TestMultiStepPlannerInit: + """Test MultiStepPlanner initialization.""" + + def test_default_max_steps(self): + """Default max_steps is 10.""" + planner = MultiStepPlanner() + assert planner.max_steps == 10 + + def test_config_max_steps(self): + """max_steps read from config.""" + planner = MultiStepPlanner(config={"max_steps": 5}) + assert planner.max_steps == 5 + + def test_empty_config_ok(self): + """Accepts None or empty config.""" + planner = MultiStepPlanner(config=None) + assert planner.config == {} + assert planner.max_steps == 10 diff --git a/tests/unit/test_prompt_builder.py b/tests/unit/test_prompt_builder.py new file mode 100644 index 0000000..e9b8e42 --- /dev/null +++ b/tests/unit/test_prompt_builder.py @@ -0,0 +1,133 @@ +"""Unit tests for PromptBuilder. + +Author: Ahmed Y. Radwan, SONIC-O1 Team +""" + +from sonic_o1_agent.processors.prompt_builder import PromptBuilder + + +class TestQueryTypeDetection: + """Test automatic query type detection.""" + + def test_detect_summarization(self): + """Test summarization query detection.""" + queries = [ + "summarize this video", + "give me a summary", + "what happened in this video", + "provide an overview", + "describe what happened", + ] + for query in queries: + result = PromptBuilder.detect_query_type(query) + assert result == "summarization", f"Failed for: {query}" + + def test_detect_temporal(self): + """Test temporal query detection.""" + queries = [ + "what happened at minute 5", + "when did they discuss X", + "between minute 2 and 3", + "what's the timestamp for Y", + "at what time did this happen", + ] + for query in queries: + result = PromptBuilder.detect_query_type(query) + assert result == "temporal", f"Failed for: {query}" + + def test_detect_qa(self): + """Test Q&A query detection.""" + queries = [ + "what is the main topic", + "who was speaking", + "where did this happen", + "how did they solve the problem", + "why did this occur", + ] + for query in queries: + result = PromptBuilder.detect_query_type(query) + assert result == "qa", f"Failed for: {query}" + + def test_detect_generic(self): + """Test generic query detection.""" + queries = [ + "tell me about this", + "analyze the content", + "help me understand", + ] + for query in queries: + result = PromptBuilder.detect_query_type(query) + assert result == "generic", f"Failed for: {query}" + + +class TestPromptBuilding: + """Test prompt construction.""" + + def test_build_generic_prompt(self): + """Test generic prompt building.""" + query = "analyze this video" + prompt = PromptBuilder.build_prompt(query) + assert query in prompt + assert isinstance(prompt, str) + + def test_build_summarization_prompt(self): + """Test summarization prompt.""" + query = "summarize" + context = {"duration_seconds": 300} + prompt = PromptBuilder.build_prompt( + query, context=context, query_type="summarization" + ) + assert "summary" in prompt.lower() + assert "300" in prompt + + def test_build_qa_prompt(self): + """Test Q&A prompt.""" + query = "What is the main topic?" + prompt = PromptBuilder.build_prompt(query, query_type="qa") + assert query in prompt + assert "answer" in prompt.lower() + + def test_build_temporal_prompt(self): + """Test temporal prompt with segment info.""" + query = "What happened here?" + context = {"segment_start": 120, "segment_end": 180} + prompt = PromptBuilder.build_prompt( + query, context=context, query_type="temporal" + ) + assert "120" in prompt + assert "180" in prompt + assert "timestamp" in prompt.lower() + + def test_prompt_with_previous_context(self): + """Test prompt includes conversation history.""" + query = "tell me more" + context = { + "previous_interactions": [ + {"query": "what happened", "response": "X occurred"} + ] + } + prompt = PromptBuilder.build_prompt(query, 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