Adding Benchmarks back into agbench and updates to agbench

python/packages/agbench/CONTRIBUTING.md

@@ -6,12 +6,11 @@ As part of the broader AutoGen project, AutoGenBench welcomes community contribu
 We ask that all contributions to AutoGenBench adhere to the following:
 
 - Follow AutoGen's broader [contribution guidelines](https://microsoft.github.io/autogen/docs/Contribute)
-- All AutoGenBench benchmarks should live in a subfolder of `/samples/tools/autogenbench/scenarios` alongside `HumanEval`, `GAIA`, etc.
+- All AutoGenBench benchmarks should live in a subfolder of `/benchmarks` alongside `HumanEval`, `GAIA`, etc.
 - Benchmark scenarios should include a detailed README.md, in the root of their folder, describing the benchmark and providing citations where warranted.
 - Benchmark data (tasks, ground truth, etc.) should be downloaded from their original sources rather than hosted in the AutoGen repository (unless the benchmark is original, and the repository *is* the original source)
     - You can use the `Scripts/init_tasks.py` file to automate this download.
-- Basic scoring should be compatible with the `autogenbench tabulate` command (e.g., by outputting logs compatible with the default tabulation mechanism, or by providing a `Scripts/custom_tabulate.py` file)
-- If you wish your benchmark to be compatible with the `autogenbench clone` command, include a `MANIFEST.json` file in the root of your folder.
+- Basic scoring should be compatible with the `agbench tabulate` command (e.g., by outputting logs compatible with the default tabulation mechanism, or by providing a `Scripts/custom_tabulate.py` file)
 
 These requirements are further detailed below, but if you simply copy the `HumanEval` folder, you will already be off to a great start.
 
@@ -62,16 +61,16 @@ For example:
 
 In this example, the string `__MODEL__` will be replaced in the file `scenarios.py`, while the string `__PROMPT__` will be replaced in the `prompt.txt` file.
 
-The `template` field can also take on a list value, but this usage is considered advanced and is not described here. See the `autogenbench/run_cmd.py` code, or the `GAIA` benchmark tasks files for additional information about this option.
+The `template` field can also take on a list value, but this usage is considered advanced and is not described here. See the `agbench/run_cmd.py` code, or the `GAIA` benchmark tasks files for additional information about this option.
 
 
 ## Task Instance Expansion Algorithm
 
-Once the tasks have been defined, as per above, they must be "instantiated" before they can be run. This instantiation happens automatically when the user issues the `autogenbench run` command and involves creating a local folder to share with Docker. Each instance and repetition gets its own folder along the path: `./results/[scenario]/[task_id]/[instance_id]`. For the sake of brevity we will refer to this folder as the `DEST_FOLDER`.
+Once the tasks have been defined, as per above, they must be "instantiated" before they can be run. This instantiation happens automatically when the user issues the `agbench run` command and involves creating a local folder to share with Docker. Each instance and repetition gets its own folder along the path: `./results/[scenario]/[task_id]/[instance_id]`. For the sake of brevity we will refer to this folder as the `DEST_FOLDER`.
 
 The algorithm for populating the `DEST_FOLDER` is as follows:
 
-1. Pre-populate DEST_FOLDER with all the basic starter files for running a scenario (found in `autogenbench/template`).
+1. Pre-populate DEST_FOLDER with all the basic starter files for running a scenario (found in `agbench/template`).
 2. Recursively copy the template folder specified in the JSONL line to DEST_FOLDER (if the JSON `template` attribute points to a folder) If the JSONs `template` attribute instead points to a file, copy the file, but rename it to `scenario.py`
 3. Apply any string replacements, as outlined in the prior section.
 4. Write a run.sh file to DEST_FOLDER that will be executed by Docker when it is loaded. The `run.sh` is described below.
@@ -139,9 +138,8 @@ echo RUN.SH COMPLETE !#!#
 Be warned that this listing is provided here for illustration purposes, and may vary over time. The source of truth are the `run.sh` files found in the ``./results/[taskset]/[task_id]/[instance_id]`` folders.
 
 
-## Integrating with the `tabulate` and `clone` commands.
-
-The above details are sufficient for defining and running tasks, but if you wish to support the `autogenbench tabulate` and `autogenbench clone` commands, a few additional steps are required.
+## Integrating with the `tabulate` 
+The above details are sufficient for defining and running tasks, but if you wish to support the `agbench tabulate`  commands, a few additional steps are required.
 
 ### Tabulations
 
@@ -154,35 +152,10 @@ Should you provide a custom tabulation script, please implement `--help` and `-h
 The `scenarios/GAIA/Scripts/custom_tabulate.py` is a great example of custom tabulation. It also shows how you can reuse some components of the default tabulator to speed up development.
 
 
-### Cloning
-
-If you wish your benchmark to be available via the `autogenbench clone` command, you will need to take three additional steps:
-
-#### Manifest
-First, provide a `MANIFEST.json` file in the root of your benchmark. An example is provided below, from which you can see the schema:
-
-```json
-{
-    "files": {
-        "Templates/TwoAgents/prompt.txt": "Templates/TwoAgents/prompt.txt",
-        "Templates/TwoAgents/coding/my_tests.py": "Templates/TwoAgents/coding/my_tests.py",
-        "Templates/TwoAgents/scenario.py": "Templates/TwoAgents/scenario.py",
-        "README.md": "README.md",
-	"Scripts/init_tasks.py": "Scripts/init_tasks.py",
-	"Scripts/custom_tabulate.py": "Scripts/custom_tabulate.py"
-    }
-}
-```
-
-The keys of the `files` dictionary are local paths, relative to your benchmark's root directory. The values are relative paths in the AutoGen GitHub repository (relative to the folder where the MANIFEST.json file is located). In most cases, the keys and values will be identical.
-
-#### SCENARIOS dictionary
-Second, you must add an entry to the `scenarios` dictionary in `autogen/samples/tools/autogenbench/scenarios/MANIFEST.json`.
 
-#### Scripts/init_tasks.py
-Finally, you should provide an `Scripts/init_tasks.py` file, in your benchmark folder, and include a `main()` method therein. This method will be loaded and called automatically by `autogenbench clone` after all manifest files have been downloaded.
+## Scripts/init_tasks.py
+Finally, you should provide an `Scripts/init_tasks.py` file, in your benchmark folder, and include a `main()` method therein. 
 
 This `init_tasks.py` script is a great place to download benchmarks from their original sources and convert them to the JSONL format required by AutoGenBench:
 - See `HumanEval/Scripts/init_tasks.py` for an example of how to expand a benchmark from an original GitHub repository.
 - See `GAIA/Scripts/init_tasks.py` for an example of how to expand a benchmark from `Hugging Face Hub`.
-- See `MATH/SCripts/init_tasks.py` for an example of how to expand a benchmark from an author-hosted website.

python/packages/agbench/README.md

@@ -1,29 +1,35 @@
 # AutoGenBench
 
-AutoGenBench is a tool for repeatedly running a set of pre-defined AutoGen tasks in a setting with tightly-controlled initial conditions. With each run, AutoGenBench will start from a blank slate. The agents being evaluated will need to work out what code needs to be written, and what libraries or dependencies to install, to solve tasks. The results of each run are logged, and can be ingested by analysis or metrics scripts (such as `autogenbench tabulate`). By default, all runs are conducted in freshly-initialized docker containers, providing the recommended level of consistency and safety.
+AutoGenBench (agbench) is a tool for repeatedly running a set of pre-defined AutoGen tasks in a setting with tightly-controlled initial conditions. With each run, AutoGenBench will start from a blank slate. The agents being evaluated will need to work out what code needs to be written, and what libraries or dependencies to install, to solve tasks. The results of each run are logged, and can be ingested by analysis or metrics scripts (such as `agbench tabulate`). By default, all runs are conducted in freshly-initialized docker containers, providing the recommended level of consistency and safety.
 
 AutoGenBench works with all AutoGen 0.1.*, and 0.2.* versions.
 
 ## Technical Specifications
 
-If you are already an AutoGenBench pro, and want the full technical specifications, please review the [contributor's guide](CONTRIBUTING.md).
-
+If you are already an AutoGenBench pro, and want the full technical specifications, please review the [contributor's guide](CONTRIBUTING.md).
 
 ## Docker Requirement
+
 AutoGenBench also requires Docker (Desktop or Engine). **It will not run in GitHub codespaces**, unless you opt for native execution (with is strongly discouraged). To install Docker Desktop see [https://www.docker.com/products/docker-desktop/](https://www.docker.com/products/docker-desktop/).
 
+If you are working in WSL, you can follow the instructions below to set up your environment:
+
+1. Install Docker Desktop. After installation, restart is needed, then open Docker Desktop, in Settings, Ressources, WSL Integration, Enable integration with additional distros – Ubuntu
+2. Clone autogen and export `AUTOGEN_REPO_BASE`. This environment variable enables the Docker containers to use the correct version agents.
+    ```bash
+    git clone [email protected]:microsoft/autogen.git
+    export AUTOGEN_REPO_BASE=<path_to_autogen>
+    ```
+
 ## Installation and Setup
 
-**To get the most out of AutoGenBench, the `autogenbench` package should be installed**. At present, the easiest way to do this is to install it via `pip`:
+[Deprecated currently] **To get the most out of AutoGenBench, the `agbench` package should be installed**. At present, the easiest way to do this is to install it via `pip`.
 
-```
-pip install autogenbench
-```
 
-If you would prefer working from source code (e.g., for development, or to utilize an alternate branch), simply clone the [AutoGen](https://github.com/microsoft/autogen) repository, then install `autogenbench` via:
+If you would prefer working from source code (e.g., for development, or to utilize an alternate branch), simply clone the [AutoGen](https://github.com/microsoft/autogen) repository, then install `agbench` via:
 
 ```
-pip install -e autogen/samples/tools/autogenbench
+pip install -e autogen/python/packages/agbench
 ```
 
 After installation, you must configure your API keys. As with other AutoGen applications, AutoGenBench will look for the OpenAI keys in the OAI_CONFIG_LIST file in the current working directory, or the OAI_CONFIG_LIST environment variable. This behavior can be overridden using a command-line parameter described later.
@@ -36,7 +42,6 @@ export OAI_CONFIG_LIST=$(cat ./OAI_CONFIG_LIST)
 
 If an OAI_CONFIG_LIST is *not* provided (by means of file or environment variable), AutoGenBench will use the OPENAI_API_KEY environment variable instead.
 
-
 For some benchmark scenarios, additional keys may be required (e.g., keys for the Bing Search API). These can be added to an `ENV.json` file in the current working folder. An example `ENV.json` file is provided below:
 
 ```
@@ -46,75 +51,106 @@ For some benchmark scenarios, additional keys may be required (e.g., keys for th
 ```
 
 ## A Typical Session
+
 Once AutoGenBench and necessary keys are installed, a typical session will look as follows:
 
-```
-autogenbench clone HumanEval
-cd HumanEval
-autogenbench run Tasks/r_human_eval_two_agents.jsonl
-autogenbench tabulate results/r_human_eval_two_agents
-```
 
-Where:
-- `autogenbench clone HumanEval` downloads and expands the HumanEval benchmark scenario.
-- `autogenbench run Tasks/r_human_eval_two_agents.jsonl` runs the tasks defined in `Tasks/r_human_eval_two_agents.jsonl`
-- `autogenbench tablue results/r_human_eval_two_agents` tabulates the results of the run
 
-Each of these commands has extensive in-line help via:
+Navigate to HumanEval
 
-- `autogenbench --help`
-- `autogenbench clone --help`
-- `autogenbench run --help`
-- `autogenbench tabulate --help`
+```bash
+cd autogen/python/packages/agbench/benchmarks/HumanEval
+```
+**Note:** The following instructions are specific to the HumanEval benchmark. For other benchmarks, please refer to the README in the respective benchmark folder, e.g.,: [AssistantBench](benchmarks/AssistantBench/README.md).
 
-**NOTE:** If you are running `autogenbench` from within the repository, you don’t need to run `autogenbench clone`. Instead, navigate to the appropriate scenario folder (e.g., `scenarios/HumanEval`) and run the `Scripts/init_tasks.py` file.
 
-More details of each command are provided in the sections that follow.
+Create a file called ENV.json with the following (required) contents (If you're using MagenticOne), if using Azure:
 
-## Cloning Benchmarks
-To clone an existing benchmark, simply run:
-```
-autogenbench clone [BENCHMARK]
+```json
+{
+    "CHAT_COMPLETION_KWARGS_JSON": "{}",
+    "CHAT_COMPLETION_PROVIDER": "azure"
+}
 ```
 
-For example,
+You can also use the openai client by replacing the last two entries in the ENV file by:
+
+- `CHAT_COMPLETION_PROVIDER='openai'`
+- `CHAT_COMPLETION_KWARGS_JSON` with the following JSON structure:
 
+```json
+{
+  "api_key": "REPLACE_WITH_YOUR_API",
+  "model": "REPLACE_WITH_YOUR_MODEL"
+}
 ```
-autogenbench clone HumanEval
+
+Now initialize the tasks.
+
+```bash
+python Scripts/init_tasks.py
 ```
 
-To see which existing benchmarks are available to clone, run:
+Note: This will attempt to download HumanEval
+
 
+Once the script completes, you should now see a folder in your current directory called `Tasks` that contains one JSONL file per template in `Templates`.
+
+Now to run a specific subset of HumanEval use:
+
+```bash
+agbench run Tasks/human_eval_MagenticOne.jsonl
 ```
-autogenbench clone --list
+
+You should see the command line print the raw logs that shows the agents in action To see a summary of the results (e.g., task completion rates), in a new terminal run the following:
+
+```bash
+agbench tabulate Results/human_eval_MagenticOne
 ```
 
-> Note: You might need to log in to HuggingFace to access certain datasets like GAIA. To do this, run `huggingface-cli login` in your terminal and follow the prompts.
+Where:
+
+- `agbench run Tasks/human_eval_MagenticOne.jsonl` runs the tasks defined in `Tasks/human_eval_MagenticOne.jsonl`
+- `agbench tablue results/human_eval_MagenticOne` tabulates the results of the run
+
+Each of these commands has extensive in-line help via:
+
+- `agbench --help`
+- `agbench run --help`
+- `agbench tabulate --help`
+- `agbench remove_missing --help`
+
+**NOTE:** If you are running `agbench` from within the repository, you need to navigate to the appropriate scenario folder (e.g., `scenarios/HumanEval`) and run the `Scripts/init_tasks.py` file.
+
+More details of each command are provided in the sections that follow.
+
 
 ## Running AutoGenBench
 
 To run a benchmark (which executes the tasks, but does not compute metrics), simply execute:
+
 ```
 cd [BENCHMARK]
-autogenbench run Tasks
+agbench run Tasks/*.jsonl
 ```
 
 For example,
+
 ```
 cd HumanEval
-autogenbench run Tasks
+agbench run Tasks/human_eval_MagenticOne.jsonl
 ```
 
 The default is to run each task once. To run each scenario 10 times, use:
 
 ```
-autogenbench run --repeat 10 Tasks
+agbench run --repeat 10 Tasks/human_eval_MagenticOne.jsonl
 ```
 
-The `autogenbench` command-line tool allows a number of command-line arguments to control various parameters of execution. Type ``autogenbench -h`` to explore these options:
+The `agbench` command-line tool allows a number of command-line arguments to control various parameters of execution. Type ``agbench -h`` to explore these options:
 
 ```
-'autogenbench run' will run the specified autogen scenarios for a given number of repetitions and record all logs and trace information. When running in a Docker environment (default), each run will begin from a common, tightly controlled, environment. The resultant logs can then be further processed by other scripts to produce metrics.
+'agbench run' will run the specified autogen scenarios for a given number of repetitions and record all logs and trace information. When running in a Docker environment (default), each run will begin from a common, tightly controlled, environment. The resultant logs can then be further processed by other scripts to produce metrics.
 
 positional arguments:
   scenario      The JSONL scenario file to run. If a directory is specified,
@@ -140,7 +176,7 @@ options:
                         The requirements file to pip install before running the scenario.
   -d DOCKER_IMAGE, --docker-image DOCKER_IMAGE
                         The Docker image to use when running scenarios. Can not be used together with --native. (default:
-                        'autogenbench:default', which will be created if not present)
+                        'agbench:default', which will be created if not present)
   --native              Run the scenarios natively rather than in docker. NOTE: This is not advisable, and should be done
                         with great caution.
 ```
@@ -171,4 +207,4 @@ Within each folder, you will find the following files:
 
 ## Contributing or Defining New Tasks or Benchmarks
 
-If you would like to develop -- or even contribute -- your own tasks or benchmarks, please review the [contributor's guide](CONTRIBUTING.md) for complete technical details.
+If you would like to develop -- or even contribute -- your own tasks or benchmarks, please review the [contributor&#39;s guide](CONTRIBUTING.md) for complete technical details.

python/packages/agbench/benchmarks/.gitignore

@@ -0,0 +1,4 @@
+*/Results/
+*/Tasks/
+*/Downloads/
+*/ENV.json

python/packages/agbench/benchmarks/AssistantBench/.gitignore

@@ -0,0 +1 @@
+ENV.json

python/packages/agbench/benchmarks/AssistantBench/ENV.json.sample

@@ -0,0 +1,5 @@
+{
+    "BING_API_KEY": "YOUR_KEY_KEY",
+    "HOMEPAGE": "https://www.bing.com/",
+    "WEB_SURFER_DEBUG_DIR": "/autogen/debug"
+}