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Merge pull request #1 from burtenshaw/fix-websearch
Fix websearch
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‎src/envs/websearch_env/README.md‎

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---
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title: Web Search Environment Server
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emoji: 📡
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colorFrom: red
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colorTo: pink
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sdk: docker
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pinned: false
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app_port: 8000
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base_path: /web
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tags:
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- openenv
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---
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# Web Search Environment
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A simple test environment that echoes back messages. Perfect for testing the env APIs as well as demonstrating environment usage patterns.
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## Quick Start
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The simplest way to use the Web Search environment is through the `WebSearchEnv` class:
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```python
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from web_search import WebSearchAction, WebSearchEnv
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try:
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# Create environment from Docker image
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web_searchenv = WebSearchEnv.from_docker_image("web_search-env:latest")
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# Reset
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result = web_searchenv.reset()
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print(f"Reset: {result.observation.echoed_message}")
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# Send multiple messages
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messages = ["Hello, World!", "Testing echo", "Final message"]
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for msg in messages:
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result = web_searchenv.step(WebSearchAction(message=msg))
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print(f"Sent: '{msg}'")
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print(f" → Echoed: '{result.observation.echoed_message}'")
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print(f" → Length: {result.observation.message_length}")
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print(f" → Reward: {result.reward}")
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finally:
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# Always clean up
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web_searchenv.close()
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```
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That's it! The `WebSearchEnv.from_docker_image()` method handles:
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- Starting the Docker container
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- Waiting for the server to be ready
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- Connecting to the environment
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- Container cleanup when you call `close()`
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## Building the Docker Image
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Before using the environment, you need to build the Docker image:
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```bash
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# From project root
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docker build -t web_search-env:latest -f server/Dockerfile .
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```
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## Deploying to Hugging Face Spaces
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You can easily deploy your OpenEnv environment to Hugging Face Spaces using the `openenv push` command:
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```bash
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# From the environment directory (where openenv.yaml is located)
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openenv push
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# Or specify options
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openenv push --namespace my-org --private
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```
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The `openenv push` command will:
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1. Validate that the directory is an OpenEnv environment (checks for `openenv.yaml`)
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2. Prepare a custom build for Hugging Face Docker space (enables web interface)
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3. Upload to Hugging Face (ensuring you're logged in)
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### Prerequisites
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- Authenticate with Hugging Face: The command will prompt for login if not already authenticated
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### Options
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- `--directory`, `-d`: Directory containing the OpenEnv environment (defaults to current directory)
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- `--repo-id`, `-r`: Repository ID in format 'username/repo-name' (defaults to 'username/env-name' from openenv.yaml)
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- `--base-image`, `-b`: Base Docker image to use (overrides Dockerfile FROM)
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- `--private`: Deploy the space as private (default: public)
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### Examples
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```bash
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# Push to your personal namespace (defaults to username/env-name from openenv.yaml)
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openenv push
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# Push to a specific repository
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openenv push --repo-id my-org/my-env
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# Push with a custom base image
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openenv push --base-image ghcr.io/meta-pytorch/openenv-base:latest
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# Push as a private space
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openenv push --private
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# Combine options
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openenv push --repo-id my-org/my-env --base-image custom-base:latest --private
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```
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After deployment, your space will be available at:
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`https://huggingface.co/spaces/<repo-id>`
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The deployed space includes:
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- **Web Interface** at `/web` - Interactive UI for exploring the environment
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- **API Documentation** at `/docs` - Full OpenAPI/Swagger interface
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- **Health Check** at `/health` - Container health monitoring
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## Environment Details
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### Action
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**WebSearchAction**: Contains a single field
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- `message` (str) - The message to echo back
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### Observation
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**WebSearchObservation**: Contains the echo response and metadata
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- `echoed_message` (str) - The message echoed back
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- `message_length` (int) - Length of the message
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- `reward` (float) - Reward based on message length (length × 0.1)
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- `done` (bool) - Always False for echo environment
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- `metadata` (dict) - Additional info like step count
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### Reward
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The reward is calculated as: `message_length × 0.1`
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- "Hi" → reward: 0.2
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- "Hello, World!" → reward: 1.3
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- Empty message → reward: 0.0
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## Advanced Usage
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### Connecting to an Existing Server
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If you already have a Web Search environment server running, you can connect directly:
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```python
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from web_search import WebSearchEnv
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# Connect to existing server
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web_searchenv = WebSearchEnv(base_url="<ENV_HTTP_URL_HERE>")
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# Use as normal
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result = web_searchenv.reset()
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result = web_searchenv.step(WebSearchAction(message="Hello!"))
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```
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Note: When connecting to an existing server, `web_searchenv.close()` will NOT stop the server.
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## Development & Testing
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### Direct Environment Testing
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Test the environment logic directly without starting the HTTP server:
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```bash
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# From the server directory
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python3 server/web_search_environment.py
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```
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This verifies that:
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- Environment resets correctly
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- Step executes actions properly
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- State tracking works
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- Rewards are calculated correctly
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### Running Locally
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Run the server locally for development:
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```bash
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uvicorn server.app:app --reload
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```
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## Project Structure
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```
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web_search/
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├── __init__.py # Module exports
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├── README.md # This file
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├── openenv.yaml # OpenEnv manifest
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├── pyproject.toml # Project metadata and dependencies
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├── uv.lock # Locked dependencies (generated)
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├── client.py # WebSearchEnv client implementation
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├── models.py # Action and Observation models
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└── server/
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├── __init__.py # Server module exports
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├── web_search_environment.py # Core environment logic
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├── app.py # FastAPI application
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└── Dockerfile # Container image definition
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```

‎src/envs/websearch_env/openenv.yaml‎

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spec_version: 1
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name: web_search
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type: space
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runtime: fastapi
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app: server.app:app
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port: 8000
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‎src/envs/websearch_env/pyproject.toml‎

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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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[build-system]
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requires = ["setuptools>=45", "wheel"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "openenv-web_search"
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version = "0.1.0"
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description = "Web Search environment for OpenEnv"
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requires-python = ">=3.10"
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dependencies = [
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# Core OpenEnv dependencies (required for server functionality)
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"openenv-core>=0.1.0",
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"fastapi>=0.115.0",
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"pydantic>=2.0.0",
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"uvicorn>=0.24.0",
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"requests>=2.31.0",
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# Environment-specific dependencies
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"chardet==5.2.0",
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"aiohttp"
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]
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[project.optional-dependencies]
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dev = [
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"pytest>=8.0.0",
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"pytest-cov>=4.0.0",
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]
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[project.scripts]
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# Server entry point - enables running via: uv run --project . server
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# or: python -m web_search.server.app
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server = "web_search.server.app:main"
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[tool.setuptools]
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package-dir = {"" = "."}
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[tool.setuptools.packages.find]
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where = ["."]
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‎src/envs/websearch_env/server/Dockerfile‎

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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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# Use the shared OpenEnv base image (Python 3.11)
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ARG BASE_IMAGE=openenv-base:latest
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# Multi-stage build using openenv-base
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# This Dockerfile is flexible and works for both:
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# - In-repo environments (with local src/core)
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# - Standalone environments (with openenv-core from pip)
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# The build script (openenv build) handles context detection and sets appropriate build args.
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ARG BASE_IMAGE=ghcr.io/meta-pytorch/openenv-base:latest
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FROM ${BASE_IMAGE} AS builder
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WORKDIR /app
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# Build argument to control whether we're building standalone or in-repo
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ARG BUILD_MODE=in-repo
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ARG ENV_NAME=web_search
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# Copy environment code (always at root of build context)
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COPY . /app/env
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# For in-repo builds, openenv-core is already in the pyproject.toml dependencies
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# For standalone builds, openenv-core will be installed from pip via pyproject.toml
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WORKDIR /app/env
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# Ensure uv is available (for local builds where base image lacks it)
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RUN if ! command -v uv >/dev/null 2>&1; then \
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curl -LsSf https://astral.sh/uv/install.sh | sh && \
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mv /root/.local/bin/uv /usr/local/bin/uv && \
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mv /root/.local/bin/uvx /usr/local/bin/uvx; \
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fi
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# Install dependencies using uv sync
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# If uv.lock exists, use it; otherwise resolve on the fly
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RUN --mount=type=cache,target=/root/.cache/uv \
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if [ -f uv.lock ]; then \
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uv sync --frozen --no-install-project --no-editable; \
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else \
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uv sync --no-install-project --no-editable; \
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fi
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RUN --mount=type=cache,target=/root/.cache/uv \
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if [ -f uv.lock ]; then \
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uv sync --frozen --no-editable; \
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else \
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uv sync --no-editable; \
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fi
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# Final runtime stage
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FROM ${BASE_IMAGE}
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# Install Python dependencies
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RUN pip install --no-cache-dir \
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chardet==5.2.0
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WORKDIR /app
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# Copy the virtual environment from builder
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COPY --from=builder /app/env/.venv /app/.venv
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# Copy the environment code
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COPY --from=builder /app/env /app/env
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# Set PATH to use the virtual environment
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ENV PATH="/app/.venv/bin:$PATH"
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# Copy OpenEnv core and WebSearch environment sources
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COPY src/core/ /app/src/core/
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COPY src/envs/websearch_env/ /app/src/envs/websearch_env/
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# Set PYTHONPATH so imports work correctly
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ENV PYTHONPATH="/app/env:$PYTHONPATH"
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# Optional: health check to ensure server responsiveness
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# Health check
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HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
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CMD curl -f http://localhost:8000/health || exit 1
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# Run the WebSearch FastAPI server
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CMD ["uvicorn", "envs.websearch_env.server.app:app", "--host", "0.0.0.0", "--port", "8000"]
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# Run the FastAPI server
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# The module path is constructed to work with the /app/env structure
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CMD ["sh", "-c", "cd /app/env && uvicorn server.app:app --host 0.0.0.0 --port 8000"]

‎src/envs/websearch_env/server/websearch_env_environment.py‎

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from models import WebSearchAction, WebSearchObservation
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from openenv_core.env_server.interfaces import Environment
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from openenv_core.env_server.types import State
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from websearch_tool import WebSearchTool
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from .websearch_tool import WebSearchTool
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class WebSearchEnvironment(Environment):

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