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Merge pull request #72 from docker/sock-store
Sock store Demo
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‎.gitignore‎

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**/.DS_Store
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**/.direnv/
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/.vscode/
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/.cursor/
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**/init-secrets.sh
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**/secret.openai-api-key

‎adk-sock-shop/.dockerignore‎

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*
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!/agents
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!/apps
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!/docker
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!/pyproject.toml
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!/uv.lock

‎adk-sock-shop/.envrc‎

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use nix

‎adk-sock-shop/.gitignore‎

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/.vscode
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/.venv
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/.mypy_cache
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/.pytest_cache
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/.ruff_cache
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/.python-version
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**/__pycache__/
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.mcp.env

‎adk-sock-shop/.mcp.env.example‎

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brave.api_key=<fill-me>
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mongodb.connection_string=mongodb://<user>:<password>@mongodb:27017/sockstore

‎adk-sock-shop/.ruff.toml‎

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[lint]
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select = ["E4", "E7", "E9", "F", "I", "RUF022"]
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[lint.isort]
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force-sort-within-sections = true

‎adk-sock-shop/CLAUDE.md‎

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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Development Commands
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### Build and Run
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```bash
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# Build and push multi-platform image (requires hydrobuild builder)
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make build-adk-image
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```
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### Development Tools
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```bash
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# Type checking
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pyright
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# Code formatting and linting
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ruff check
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ruff format
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```
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### Testing and Development
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- Access the web interface at `http://localhost:8080` after running `docker compose up --build`
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- Use Google Cloud Run deployment with `compose.gcloudrun.yaml` for cloud deployment
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## Architecture Overview
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This is an **ADK (Agent Development Kit) multi-agent fact-checking system** with three coordinated agents:
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### Agent Hierarchy
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- **Root Agent**: `llm_auditor` (SequentialAgent) at `agents/agent.py:22`
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- Orchestrates the entire fact-checking workflow
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- Coordinates between Critic and Reviser agents sequentially
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- **Critic Agent**: `agents/sub_agents/critic/agent.py:27`
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- Has access to DuckDuckGo search via MCP (Model Context Protocol)
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- Gathers external evidence to support or refute claims
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- Uses `mcp/duckduckgo:search` toolset
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- **Reviser Agent**: `agents/sub_agents/reviser/agent.py:97`
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- No external tools - pure reasoning agent
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- Refines conclusions based on Critic's findings
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- Uses content processing callbacks for model compatibility
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### Agent Communication Pattern
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1. User submits question → Auditor
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2. Auditor → Critic (with search tools)
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3. Critic gathers evidence → Auditor
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4. Auditor → Reviser (reasoning only)
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5. Reviser refines conclusion → Auditor
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6. Auditor delivers final answer
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### Special Implementation Notes
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- Reviser agent uses content preprocessing callbacks (`force_string_content`, `_remove_end_of_edit_mark`) for model compatibility
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- MCP tools are configured via `create_mcp_toolsets()` in critic agent
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- All agents use LiteLLM for model abstraction with OpenAI format
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### Generating a compose.yaml file
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* To add models and gateways to an existing compose.yaml file, you should figure out which model you want to use, and which mcp servers are needed.
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* Create a service entry like the following
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```
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mcp-gateway:
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image: docker/mcp-gateway:latest
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use_api_socket: true
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command:
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- --transport=sse
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- --servers=server1,server2,server3
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- --config=/mcp_config
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- --secrets=docker-desktop:/run/secrets/mcp_secret
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secrets:
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- mcp_secret
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```
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but replace the servers value with a comma-separate list of the MCP servers that you want to use.
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* Also, if there is no top-level `secrets` entry with a `mcp_secret` entry then add one. It should look like:
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```
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secrets:
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mcp_secret:
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file: ./.mcp.env
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```
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and remind that the user that Docker offload will require secrets to be stored in a local file named .mcp.env
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* Whenever a model is needed, add a toplevel entry in the compose.yaml file with the name of the model. It should look like:
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```
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models:
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<model_name>:
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model: <model_image_ref>
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```
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but replace the <model_name> with whatever model the user wants to use.
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If the user wnts the model_name qwen3 then the model_image_ref should be ai/qwen3:14B-Q6_K
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* whenever a model is added, the user must specify what service needs the model.
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Add a new entry to that service's definition with the following content.
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```
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models:
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<model_name>:
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endpoint_var: MODEL_RUNNER_URL
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model_var: MODEL_RUNNER_MODEL
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```
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If it's unclear which service needs this definition then ask. Always add the model to just the service definition that needs it.

‎adk-sock-shop/Dockerfile‎

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# Use Python 3.11 slim image as base
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FROM python:3.13-slim
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ENV PYTHONUNBUFFERED=1
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RUN pip install uv
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WORKDIR /app
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# Install system dependencies
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COPY pyproject.toml uv.lock ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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UV_COMPILE_BYTECODE=1 uv pip install --system .
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# Copy application code
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COPY agents/ ./agents/
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RUN python -m compileall -q .
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COPY <<EOF /entrypoint.sh
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#!/bin/sh
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set -e
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# if test -f /run/secrets/openai-api-key; then
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# export OPENAI_API_KEY=$(cat /run/secrets/openai-api-key)
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# fi
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# if test -n "\${OPENAI_API_KEY}"; then
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# echo "Using OpenAI with \${OPENAI_MODEL_NAME}"
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# else
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echo "Using Docker Model Runner with \${MODEL_RUNNER_MODEL}"
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export OPENAI_MODEL_NAME=openai/\${MODEL_RUNNER_MODEL}
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# fi
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exec adk api_server --host 0.0.0.0 --port 8000 --log_level DEBUG
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EOF
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RUN chmod +x /entrypoint.sh
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# Create non-root user
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RUN useradd --create-home --shell /bin/bash app \
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&& chown -R app:app /app
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USER app
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ENTRYPOINT [ "/entrypoint.sh" ]

‎adk-sock-shop/Dockerfile.adk-ui‎

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# Use Python 3.11 slim image as base
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FROM python:3.13-slim
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ENV PYTHONUNBUFFERED=1
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RUN pip install uv
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WORKDIR /app
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# Install system dependencies
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COPY pyproject.toml uv.lock ./
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RUN --mount=type=cache,target=/root/.cache/uv \
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UV_COMPILE_BYTECODE=1 uv pip install --system .
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# Copy application code
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COPY apps/ ./apps/
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RUN python -m compileall -q .
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# Create non-root user
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RUN useradd --create-home --shell /bin/bash app \
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&& chown -R app:app /app
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USER app
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CMD ["streamlit", "run", "apps/vendor_app.py", "--server.port=3000"]

‎adk-sock-shop/Makefile‎

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build-adk-image:
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docker buildx build --builder hydrobuild --platform linux/amd64,linux/arm64 --tag jimclark106/supplier_agent:latest --push --file Dockerfile .
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build-adk-ui-image:
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docker buildx build --builder hydrobuild --platform linux/amd64,linux/arm64 --tag jimclark106/supplier_agent_ui:latest --push --file Dockerfile.adk-ui .
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gateway-secrets:
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docker mcp secret set 'mongodb.connection_string=mongodb://admin:password@mongodb:27017/' && \
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docker mcp secret set 'resend.api_key=$(RESEND_API_KEY)' && \
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docker mcp secret set 'brave.api_key=$(BRAVE_API_KEY)' && \
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docker mcp secret export brave resend mongodb > ./.mcp.env && \
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echo $(OPENAI_API_KEY) > ./secret.openai-api-key
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adk-api-server-start:
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MCPGATEWAY_ENDPOINT=http://localhost:8811/sse \
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MODEL_RUNNER_URL=http://localhost:12434/engines/llama.cpp/v1 \
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MODEL_RUNNER_MODEL=ai/qwen3:14B-Q6_K \
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uv run adk api_server --port 8000 --log_level DEBUG
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adk-ui-start:
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API_BASE_URL=http://localhost:8000 \
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uv run streamlit run apps/vendor_app.py --server.port 3000
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local-context:
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docker context use desktop-linux
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local-compose-up:
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docker compose up front-end catalogue catalogue-db mongodb mcp-gateway
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local-down:
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docker compose down
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local-up: local-context local-compose-up
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local-down: local-context local-down
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offload-context:
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docker context use docker-cloud
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offload-compose-up:
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docker compose -f compose.yaml -f compose.offload.yaml up --build
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offload-up: offload-context offload-compose-up
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offload-down:
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docker compose -f compose.yaml -f compose.offload.yaml down
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