extract code from text; solve_problem; request_timeout in config; improve code (#999)

* extract code from text

* solve_problem; request_timeout in config

* improve

* move import statement

* improve code

* generate assertions

* constant

* configs for implement; voting

* doc

* execute code in docker

* success indicator of code executation in docker

* success indicator

* execute code

* strip n

* add cost in generate_code

* add docstr

* filename

* bytes

* check docker version

* print log

* python test

* remove api key address

* rename exit code

* success exit code

* datasets

* exit code

* recover openai tests

* cache and pattern match

* wait

* wait

* cache and test

* timeout test

* python image name and skip macos

* windows image

* docker images

* volume path and yaml

* win path -> posix

* extensions

* path

* path

* path

* path

* path

* path

* path

* path

* path

* path

* path

* skip windows

* path

* timeout in windows

* use_docker

* use_docker

* hot fix from #1000

---------

Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>

.github/workflows/openai.yml

@@ -29,10 +29,10 @@ jobs:
           python-version: ${{ matrix.python-version }}
       - name: Install packages and dependencies
         run: |
+          docker --version
           python -m pip install --upgrade pip wheel
-          pip install -e .
+          pip install -e .[autogen,blendsearch]
           python -c "import flaml"
-          pip install -e .[openai]
       - name: Coverage
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}

README.md

@@ -3,8 +3,8 @@
 [![Build](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml/badge.svg)](https://github.com/microsoft/FLAML/actions/workflows/python-package.yml)
 ![Python Version](https://img.shields.io/badge/3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue)
 [![Downloads](https://pepy.tech/badge/flaml)](https://pepy.tech/project/flaml)
-<!-- [![Join the chat at https://gitter.im/FLAMLer/community](https://badges.gitter.im/FLAMLer/community.svg)](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) -->
 [![](https://img.shields.io/discord/1025786666260111483?logo=discord&style=flat)](https://discord.gg/Cppx2vSPVP)
+<!-- [![Join the chat at https://gitter.im/FLAMLer/community](https://badges.gitter.im/FLAMLer/community.svg)](https://gitter.im/FLAMLer/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) -->
 
 
 # A Fast Library for Automated Machine Learning & Tuning

flaml/autogen/__init__.py

@@ -0,0 +1,2 @@
+DEFAULT_MODEL = "gpt-4"
+FAST_MODEL = "gpt-3.5-turbo"

flaml/autogen/code_utils.py

@@ -1,56 +1,287 @@
 import signal
 import subprocess
 import sys
+import os
+import pathlib
 from typing import List, Dict, Tuple, Optional, Union, Callable
-from flaml import oai
+import re
+import time
+from flaml.autogen import oai, DEFAULT_MODEL, FAST_MODEL
+
+# Regular expression for finding a code block
+CODE_BLOCK_PATTERN = r"```\w*\n(.*?)\n```"
+WORKING_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "extensions")
+
+
+def extract_code(text: str, pattern: str = CODE_BLOCK_PATTERN) -> str:
+    # Use a regular expression to find the code block
+    match = re.search(pattern, text, flags=re.DOTALL)
+    # If a match is found, return the code
+    if match:
+        return match.group(1)
+    # If no code block is found, return the whole text
+    return text
+
+
+def generate_code(pattern: str = CODE_BLOCK_PATTERN, **config) -> Tuple[str, float]:
+    """Generate code.
+
+    Args:
+        pattern (Optional, str): The regular expression pattern for finding the code block.
+            The default pattern is for finding a code block in a markdown file.
+        config (Optional, dict): The configuration for the API call.
+
+    Returns:
+        str: The generated code.
+        float: The cost of the generation.
+    """
+    response = oai.Completion.create(**config)
+    cost = oai.Completion.cost(config["model"], response)
+    return extract_code(oai.Completion.extract_text(response)[0], pattern), cost
+
+
+_IMPROVE_FUNCTION_CONFIG = {
+    "prompt": """Improve the function '{func_name}' to achieve the objective '{objective}'.
+The current implementation of the function is as follows:
+{file_string}""",
+    "model": DEFAULT_MODEL,
+    "request_timeout": 300,
+}
+
+
+def improve_function(file_name, func_name, objective, **config):
+    """(work in progress) Improve the function to achieve the objective."""
+    params = {**_IMPROVE_FUNCTION_CONFIG, **config}
+    # read the entire file into a str
+    with open(file_name, "r") as f:
+        file_string = f.read()
+    response = oai.Completion.create(
+        {"func_name": func_name, "objective": objective, "file_string": file_string}, **params
+    )
+    cost = oai.Completion.cost(params["model"], response)
+    return oai.Completion.extract_text(response)[0], cost
+
+
+_IMPROVE_CODE_CONFIG = {
+    "prompt": """Analyze the code in the following files and return a list of suggestions for improvement{followup}, to achieve the objective of '{objective}'.
+{code}
+""",
+    "model": DEFAULT_MODEL,
+    "request_timeout": 900,
+}
+
+
+def improve_code(files, objective, suggest_only=True, **config):
+    """Improve the code to achieve a given objective.
+
+    Args:
+        files (list): A list of file names containing the source code.
+        objective (str): The objective to achieve.
+        suggest_only (bool): Whether to return only the suggestions or the improved code.
+        config (Optional, dict): The configuration for the API call.
+
+    Returns:
+        str: The improved code if suggest_only=False; a list of suggestions if suggest_only=True (default).
+        float: The cost of the generation.
+    """
+    code = ""
+    for file_name in files:
+        # read the entire file into a string
+        with open(file_name, "r") as f:
+            file_string = f.read()
+        code += f"""{file_name}:
+{file_string}
+
+"""
+    params = {**_IMPROVE_CODE_CONFIG, **config}
+    followup = "" if suggest_only else " followed by the improved code"
+    response = oai.Completion.create({"objective": objective, "code": code, "followup": followup}, **params)
+    cost = oai.Completion.cost(params["model"], response)
+    return oai.Completion.extract_text(response)[0], cost
 
 
 def timeout_handler(signum, frame):
     raise TimeoutError("Timed out!")
 
 
-def execute_code(code: str, max_exec_time: Optional[int] = 3):
+def execute_code(
-    signal.signal(signal.SIGALRM, timeout_handler)
+    code: Optional[str] = None,
+    timeout: Optional[int] = 600,
+    filename: Optional[str] = None,
+    work_dir: Optional[str] = None,
+    use_docker: Optional[bool] = True,
+) -> Tuple[int, bytes]:
+    """Execute code in a docker container.
+    This function is not tested on MacOS.
+
+    Args:
+        code (Optional, str): The code to execute.
+            If None, the code from the file specified by filename will be executed.
+            Either code or filename must be provided.
+        timeout (Optional, int): The maximum execution time in seconds.
+        filename (Optional, str): The file name to save the code or where the code is stored when `code` is None.
+            If None, a file with a randomly generated name will be created.
+            The randomly generated file will be deleted after execution.
+            The file name must be a relative path. Relative paths are relative to the working directory.
+        work_dir (Optional, str): The working directory for the code execution.
+            If None, a default working directory will be used.
+            The default working directory is the "extensions" directory under
+            "xxx/flaml/autogen", where "xxx" is the path to the flaml package.
+        use_docker (Optional, bool): Whether to use a docker container for code execution.
+            If True, the code will be executed in a docker container.
+            If False, the code will be executed in the current environment.
+            Default is True. If the code is executed in the current environment,
+            the code must be trusted.
+
+    Returns:
+        int: 0 if the code executes successfully.
+        bytes: The error message if the code fails to execute; the stdout otherwise.
+    """
+    assert code is not None or filename is not None, "Either code or filename must be provided."
+
+    original_filename = filename
+    if filename is None:
+        code_hash = hash(code)
+        # create a file with a automatically generated name
+        filename = f"tmp_code_{code_hash}.py"
+    if work_dir is None:
+        work_dir = WORKING_DIR
+    filepath = os.path.join(work_dir, filename)
+    file_dir = os.path.dirname(filepath)
+    os.makedirs(file_dir, exist_ok=True)
+
+    if code is not None:
         code = code.strip()
-    with open("codetest.py", "w") as fout:
+        with open(filepath, "w") as fout:
             fout.write(code)
+    # check if already running in a docker container
+    in_docker_container = os.path.exists("/.dockerenv")
+    if not use_docker or in_docker_container:
+        # already running in a docker container
+        signal.signal(signal.SIGALRM, timeout_handler)
         try:
-        signal.alarm(max_exec_time)
+            signal.alarm(timeout)
+            # run the code in a subprocess in the current docker container in the working directory
             result = subprocess.run(
-            [sys.executable, "codetest.py"],
+                [sys.executable, filename],
-            stdout=subprocess.DEVNULL,
+                cwd=work_dir,
-            stderr=subprocess.PIPE,
+                capture_output=True,
             )
             signal.alarm(0)
         except TimeoutError:
-        return 0
+            if original_filename is None:
-    return int(result.returncode == 0)
+                os.remove(filepath)
+            return 1, "Timeout"
+        if original_filename is None:
+            os.remove(filepath)
+        return result.returncode, result.stderr if result.returncode else result.stdout
+
+    import docker
+    from requests.exceptions import ReadTimeout, ConnectionError
+
+    # create a docker client
+    client = docker.from_env()
+    image_list = ["python:3-alpine", "python:3", "python:3-windowsservercore"]
+    for image in image_list:
+        # check if the image exists
+        try:
+            client.images.get(image)
+            break
+        except docker.errors.ImageNotFound:
+            # pull the image
+            print("Pulling image", image)
+            try:
+                client.images.pull(image)
+                break
+            except docker.errors.DockerException:
+                print("Failed to pull image", image)
+    # get a randomized str based on current time to wrap the exit code
+    exit_code_str = f"exitcode{time.time()}"
+    abs_path = pathlib.Path(work_dir).absolute()
+    # if sys.platform == "win32":
+    #     abs_path = str(abs_path).replace("\\", "/")
+    #     abs_path = f"/{abs_path[0].lower()}{abs_path[2:]}"
+    # create a docker container
+    container = client.containers.run(
+        image,
+        command=[
+            "sh",
+            "-c",
+            f"python {filename}; exit_code=$?; echo -n {exit_code_str}; echo -n $exit_code; echo {exit_code_str}",
+        ],
+        working_dir="/workspace",
+        detach=True,
+        # get absolute path to the working directory
+        volumes={abs_path: {"bind": "/workspace", "mode": "rw"}},
+    )
+    start_time = time.time()
+    while container.status != "exited" and time.time() - start_time < timeout:
+        # Reload the container object
+        container.reload()
+    if container.status != "exited":
+        container.stop()
+        container.remove()
+        if original_filename is None:
+            os.remove(filepath)
+        return 1, "Timeout"
+    # try:
+    #     container.wait(timeout=timeout)
+    # except (ReadTimeout, ConnectionError):
+    #     container.stop()
+    #     container.remove()
+    #     if original_filename is None:
+    #         os.remove(filepath)
+    #     return 1, "Timeout"
+    # get the container logs
+    logs = container.logs().decode("utf-8").rstrip()
+    # remove the container
+    container.remove()
+    # check if the code executed successfully
+    exit_code = container.attrs["State"]["ExitCode"]
+    if exit_code == 0:
+        # extract the exit code from the logs
+        pattern = re.compile(f"{exit_code_str}(\\d+){exit_code_str}")
+        match = pattern.search(logs)
+        exit_code = int(match.group(1))
+        # remove the exit code from the logs
+        logs = pattern.sub("", logs)
+
+    logs = bytes(logs, "utf-8")
+    if original_filename is None:
+        os.remove(filepath)
+    # return the exit code and logs
+    return exit_code, logs
 
 
-def generate_assertions(definition: str, model: Optional[str] = "gpt-3.5-turbo") -> Tuple[str, float]:
+_GENERATE_ASSERTIONS_CONFIG = {
+    "prompt": """Given the signature and docstring, write the exactly same number of assertion(s) for the provided example(s) in the docstring, without assertion messages.
+
+func signature:
+{definition}
+assertions:""",
+    "model": FAST_MODEL,
+    "max_tokens": 256,
+    "stop": "\n\n",
+}
+
+
+def generate_assertions(definition: str, **config) -> Tuple[str, float]:
     """Generate assertions for a function.
 
     Args:
         definition (str): The function definition, including the signature and docstr.
-        model (str): The model used for generation.
+        config (Optional, dict): The configuration for the API call.
 
     Returns:
         str: The generated assertions.
         float: The cost of the generation.
     """
-    prompt = """Given the signature and docstring, write the exactly same number of assertion(s) for the provided example(s) in the docstring, without assertion messages.
+    params = {**_GENERATE_ASSERTIONS_CONFIG, **config}
-
-func signature:
-{definition}
-assertions:"""
     response = oai.Completion.create(
         {"definition": definition},
-        model=model,
+        **params,
-        prompt=prompt,
-        max_tokens=256,
-        stop="\n\n",
     )
-    cost = oai.Completion.cost(model, response)
+    cost = oai.Completion.cost(params["model"], response)
     assertions = oai.Completion.extract_text(response)[0]
     return assertions, cost
 
@@ -70,6 +301,8 @@ def eval_function_completions(
     test: Optional[str] = None,
     entry_point: Optional[str] = None,
     assertions: Optional[Union[str, Callable[[str], Tuple[str, float]]]] = None,
+    timeout: Optional[float] = 3,
+    use_docker: Optional[bool] = True,
 ) -> Dict:
     """Select a response from a list of responses for the function completion task (using generated assertions), and/or evaluate if the task is successful using a gold test.
 
@@ -80,6 +313,7 @@ def eval_function_completions(
         entry_point (Optional, str): The name of the function.
         assertions (Optional, str or Callable): The assertion code which serves as a filter of the responses, or an assertion generator.
             When provided, only the responses that pass the assertions will be considered for the actual test (if provided).
+        timeout (Optional, float): The timeout for executing the code.
 
     Returns:
         dict: The success metrics.
@@ -95,7 +329,7 @@ def eval_function_completions(
                 if response.startswith("def")
                 else f"{definition}{response}\n{test}\ncheck({entry_point})"
             )
-            success = execute_code(code)
+            success = execute_code(code, timeout=timeout, use_docker=use_docker)[0] == 0
             success_list.append(success)
         return {
             "expected_success": 1 - pow(1 - sum(success_list) / n, n),
@@ -112,7 +346,7 @@ def eval_function_completions(
             code = (
                 f"{response}\n{assertions}" if response.startswith("def") else f"{definition}{response}\n{assertions}"
             )
-            succeed_assertions = execute_code(code)
+            succeed_assertions = execute_code(code, timeout=timeout, use_docker=use_docker)[0] == 0
             if succeed_assertions:
                 break
     else:
@@ -132,7 +366,7 @@ def eval_function_completions(
         if response.startswith("def")
         else f"{definition}{response}\n{test}\ncheck({entry_point})"
     )
-    success = execute_code(code_test)
+    success = execute_code(code_test, timeout=timeout, use_docker=use_docker)[0] == 0
     return {
         "index_selected": i,
         "succeed_assertions": succeed_assertions,
@@ -142,9 +376,20 @@ def eval_function_completions(
     }
 
 
+_FUNC_COMPLETION_PROMPT = "# Python 3{definition}"
+_FUNC_COMPLETION_STOP = ["\nclass", "\ndef", "\nif", "\nprint"]
+_IMPLEMENT_CONFIGS = [
+    {"model": FAST_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "temperature": 0, "seed": 0},
+    {"model": FAST_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 7, "seed": 0},
+    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "temperature": 0, "seed": 1},
+    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 2, "seed": 2},
+    {"model": DEFAULT_MODEL, "prompt": _FUNC_COMPLETION_PROMPT, "stop": _FUNC_COMPLETION_STOP, "n": 1, "seed": 2},
+]
+
+
 def implement(
     definition: str,
-    configs: List[Dict],
+    configs: Optional[List[Dict]] = None,
     assertions: Optional[Union[str, Callable[[str], Tuple[str, float]]]] = generate_assertions,
 ) -> Tuple[str, float]:
     """Implement a function from a definition.
@@ -160,6 +405,7 @@ def implement(
         int: The index of the configuration which generates the implementation.
     """
     cost = 0
+    configs = configs or _IMPLEMENT_CONFIGS
     if len(configs) > 1 and callable(assertions):
         assertions, cost = assertions(definition)
     for i, config in enumerate(configs):

flaml/autogen/math_utils.py

@@ -1,4 +1,28 @@
 from typing import Optional
+from flaml.autogen import oai, DEFAULT_MODEL
+
+_MATH_PROMPT = "{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \\boxed{{}}."
+_MATH_CONFIG = {
+    "model": DEFAULT_MODEL,
+    "prompt": _MATH_PROMPT,
+}
+
+
+def solve_problem(problem: str, **config) -> str:
+    """(work in progress) Solve the math problem.
+
+    Args:
+        problem (str): The problem statement.
+        config (Optional, dict): The configuration for the API call.
+
+    Returns:
+        str: The solution to the problem.
+    """
+    params = {**_MATH_CONFIG, **config}
+    response = oai.Completion.create({"problem": problem}, **params)
+    cost = oai.Completion.cost(params["model"], response)
+    results = eval_math_responses(oai.Completion.extract_text(response))
+    return results.get("voted_answer"), cost
 
 
 def remove_boxed(string: str) -> Optional[str]:

flaml/autogen/oai/completion.py

@@ -145,9 +145,10 @@ class Completion:
         request_timeout = cls.request_timeout
         while True:
             try:
+                if "request_timeout" in config:
+                    response = openai_completion.create(**config)
+                else:
                     response = openai_completion.create(request_timeout=request_timeout, **config)
-                cls._cache.set(key, response)
-                return response
             except (
                 ServiceUnavailableError,
                 APIError,
@@ -170,6 +171,8 @@ class Completion:
                 else:
                     break
                 if isinstance(e, Timeout):
+                    if "request_timeout" in config:
+                        raise
                     request_timeout <<= 1
                 request_timeout = min(request_timeout, time_left)
                 sleep(cls.retry_time)
@@ -180,10 +183,15 @@ class Completion:
                     config["engine"] = config.pop("model").replace("gpt-3.5-turbo", "gpt-35-turbo")
                 else:
                     raise
+            else:
+                if use_cache:
+                    cls._cache.set(key, response)
+                return response
         logger.warning(
             f"Failed to get response from openai api due to getting RateLimitError or Timeout for {cls.retry_timeout} seconds."
         )
         response = -1
+        if use_cache:
             cls._cache.set(key, response)
         return response
 

flaml/version.py

@@ -1 +1 @@
-__version__ = "1.2.1"
+__version__ = "1.2.2"

notebook/autogen_chatgpt.ipynb

@@ -21,9 +21,9 @@
     "\n",
     "## Requirements\n",
     "\n",
-    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai,blendsearch] option:\n",
     "```bash\n",
-    "pip install flaml[openai]==1.2.0\n",
+    "pip install flaml[openai,blendsearch]==1.2.1\n",
     "```"
    ]
   },
@@ -40,7 +40,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[openai]==1.2.0 datasets"
+    "# %pip install flaml[openai,blendsearch]==1.2.1 datasets"
    ]
   },
   {

notebook/autogen_openai.ipynb

@@ -21,9 +21,9 @@
     "\n",
     "## Requirements\n",
     "\n",
-    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen,blendsearch] option:\n",
     "```bash\n",
-    "pip install flaml[openai]==1.2.0\n",
+    "pip install flaml[autogen,blendsearch]==1.2.1\n",
     "```"
    ]
   },
@@ -40,7 +40,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[openai]==1.2.0 datasets"
+    "# %pip install flaml[autogen,blendsearch]==1.2.1 datasets"
    ]
   },
   {
@@ -297,7 +297,13 @@
     "from functools import partial\n",
     "from flaml.autogen.code_utils import eval_function_completions, generate_assertions\n",
     "\n",
-    "eval_with_generated_assertions = partial(eval_function_completions, assertions=generate_assertions)"
+    "eval_with_generated_assertions = partial(\n",
+    "    eval_function_completions,\n",
+    "    assertions=generate_assertions,\n",
+    "    use_docker=False,\n",
+    "    # Please set use_docker=True if you have docker available to run the generated code.\n",
+    "    # Using docker is safer than running the generated code directly.\n",
+    ")\n"
    ]
   },
   {

notebook/research/autogen_code.ipynb

@@ -19,9 +19,9 @@
     "\n",
     "## Requirements\n",
     "\n",
-    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
+    "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen] option:\n",
     "```bash\n",
-    "pip install flaml[openai]==1.2.0\n",
+    "pip install flaml[autogen]==1.2.1\n",
     "```"
    ]
   },
@@ -38,7 +38,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[openai]==1.2.0 datasets"
+    "# %pip install flaml[autogen]==1.2.1 datasets"
    ]
   },
   {
@@ -381,7 +381,7 @@
     "success = 0\n",
     "for i, d in enumerate(data):\n",
     "    response, cost_i, j = implement(d[\"definition\"], configs)\n",
-    "    metrics = eval_function_completions(responses=[response], **d)\n",
+    "    metrics = eval_function_completions(responses=[response], use_docker=False, **d)\n",
     "    success += metrics[\"success\"]\n",
     "    cost += cost_i\n",
     "    print(f\"Example {i}, config {j}, success {success}\")\n",

notebook/research/math_level5counting.ipynb

@@ -21,7 +21,7 @@
     "\n",
     "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
     "```bash\n",
-    "pip install flaml[openai]==1.2.0\n",
+    "pip install flaml[openai]==1.2.1\n",
     "```"
    ]
   },
@@ -38,7 +38,7 @@
    },
    "outputs": [],
    "source": [
-    "# %pip install flaml[openai]==1.2.0 datasets"
+    "# %pip install flaml[openai]==1.2.1 datasets"
    ]
   },
   {

setup.py

@@ -120,7 +120,8 @@ setuptools.setup(
             "pytorch-forecasting>=0.9.0",
         ],
         "benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3"],
-        "openai": ["openai==0.27.4", "diskcache", "optuna==2.8.0"],
+        "openai": ["openai==0.27.4", "diskcache"],
+        "autogen": ["openai==0.27.4", "diskcache", "docker"],
         "synapse": ["joblibspark>=0.5.0", "optuna==2.8.0", "pyspark>=3.2.0"],
     },
     classifiers=[

test/openai/test_completion.py

@@ -8,8 +8,70 @@ from flaml.autogen.code_utils import (
     eval_function_completions,
     generate_assertions,
     implement,
+    generate_code,
+    extract_code,
+    improve_function,
+    improve_code,
+    execute_code,
 )
-from flaml.autogen.math_utils import eval_math_responses
+from flaml.autogen.math_utils import eval_math_responses, solve_problem
+
+
+@pytest.mark.skipif(
+    sys.platform in ["darwin", "win32"],
+    reason="do not run on MacOS or windows",
+)
+def test_execute_code():
+    try:
+        import docker
+    except ImportError as exc:
+        print(exc)
+        return
+    exitcode, msg = execute_code("print('hello world')", filename="tmp/codetest.py")
+    assert exitcode == 0 and msg == b"hello world\n", msg
+    # read a file
+    print(execute_code("with open('tmp/codetest.py', 'r') as f: a=f.read()"))
+    # create a file
+    print(execute_code("with open('tmp/codetest.py', 'w') as f: f.write('b=1')", work_dir="test/openai/my_tmp"))
+    # execute code in a file
+    print(execute_code(filename="tmp/codetest.py"))
+    # execute code for assertion error
+    exit_code, msg = execute_code("assert 1==2")
+    assert exit_code, msg
+    # execute code which takes a long time
+    exit_code, error = execute_code("import time; time.sleep(2)", timeout=1)
+    assert exit_code and error == "Timeout"
+    exit_code, error = execute_code("import time; time.sleep(2)", timeout=1, use_docker=False)
+    assert exit_code and error == "Timeout"
+
+
+def test_improve():
+    try:
+        import openai
+        import diskcache
+    except ImportError as exc:
+        print(exc)
+        return
+    improved, _ = improve_function(
+        "flaml/autogen/math_utils.py",
+        "solve_problem",
+        "Solve math problems accurately, by avoiding calculation errors and reduce reasoning errors.",
+    )
+    with open("test/openai/math_utils.py.improved", "w") as f:
+        f.write(improved)
+    suggestion, _ = improve_code(
+        ["flaml/autogen/code_utils.py", "flaml/autogen/math_utils.py"],
+        "leverage generative AI smartly and cost-effectively",
+    )
+    print(suggestion)
+    improvement, cost = improve_code(
+        ["flaml/autogen/code_utils.py", "flaml/autogen/math_utils.py"],
+        "leverage generative AI smartly and cost-effectively",
+        suggest_only=False,
+    )
+    print(cost)
+    with open("test/openai/suggested_improvement.txt", "w") as f:
+        f.write(improvement)
 
 
 def test_nocontext():
@@ -19,8 +81,59 @@ def test_nocontext():
     except ImportError as exc:
         print(exc)
         return
-    response = oai.Completion.create(model="text-ada-001", prompt="1+1=", max_tokens=1)
+    response = oai.Completion.create(
+        model="text-ada-001", prompt="1+1=", max_tokens=1, use_cache=False, request_timeout=10
+    )
     print(response)
+    code, _ = generate_code(
+        model="gpt-3.5-turbo",
+        messages=[
+            {
+                "role": "system",
+                "content": "You want to become a better assistant by learning new skills and improving your existing ones.",
+            },
+            {
+                "role": "user",
+                "content": "Write reusable code to use web scraping to get information from websites.",
+            },
+        ],
+    )
+    print(code)
+    # test extract_code from markdown
+    code = extract_code(
+        """
+Example:
+```
+print("hello extract code")
+```
+"""
+    )
+    print(code)
+
+    code = extract_code(
+        """
+Example:
+```python
+def scrape(url):
+    import requests
+    from bs4 import BeautifulSoup
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, "html.parser")
+    title = soup.find("title").text
+    text = soup.find("div", {"id": "bodyContent"}).text
+    return title, text
+```
+Test:
+```python
+url = "https://en.wikipedia.org/wiki/Web_scraping"
+title, text = scrape(url)
+print(f"Title: {title}")
+print(f"Text: {text}")
+"""
+    )
+    print(code)
+    solution, cost = solve_problem("1+1=")
+    print(solution, cost)
 
 
 @pytest.mark.skipif(
@@ -102,6 +215,7 @@ def test_humaneval(num_samples=1):
         inference_budget=0.002,
         optimization_budget=2,
         num_samples=num_samples,
+        # logging_level=logging.INFO,
         prompt=[
             "{definition}",
             "# Python 3{definition}",
@@ -175,12 +289,10 @@ def test_math(num_samples=-1):
     }
     test_data_sample = test_data[0:3]
     result = oai.ChatCompletion.test(test_data_sample, vanilla_config, eval_math_responses)
-    test_data_sample = test_data[3:6]
     result = oai.ChatCompletion.test(
         test_data_sample,
         vanilla_config,
         eval_math_responses,
-        use_cache=False,
         agg_method="median",
     )
 
@@ -194,14 +306,12 @@ def test_math(num_samples=-1):
         test_data_sample,
         vanilla_config,
         eval_math_responses,
-        use_cache=False,
         agg_method=my_median,
     )
     result = oai.ChatCompletion.test(
         test_data_sample,
         vanilla_config,
         eval_math_responses,
-        use_cache=False,
         agg_method={
             "expected_success": my_median,
             "success": my_average,
@@ -231,9 +341,11 @@ def test_math(num_samples=-1):
 
 
 if __name__ == "__main__":
-    import openai
+    # import openai
 
-    openai.api_key_path = "test/openai/key.txt"
+    # openai.api_key_path = "test/openai/key.txt"
-    test_nocontext()
+    test_execute_code()
-    test_humaneval(1)
+    # test_improve()
-    test_math(1)
+    # test_nocontext()
+    # test_humaneval(1)
+    # test_math(1)

website/docs/Examples/AutoGen-OpenAI.md

@@ -5,9 +5,9 @@ In this example, we will tune several hyperparameters for the OpenAI's completio
 
 ### Prerequisites
 
-Install the [openai] option. The OpenAI integration is in preview.
+Install the [autogen,blendsearch] option. The OpenAI integration is in preview.
 ```bash
-pip install "flaml[openai]==1.2.0"
+pip install "flaml[autogen,blendsearch]==1.2.1 datasets"
 ```
 
 Setup your OpenAI key:

website/docs/Use-Cases/Auto-Generation.md

@@ -126,12 +126,29 @@ response = oai.Completion.create(problme=problem, prompt="{problem} Solve the pr
 ```
 
 ## Other utilities
-`flaml.oai.Completion` also offers some additional utilities, such as:
+
+### Completion
+
+[`flaml.oai.Completion`](../reference/autogen/oai/completion) also offers some additional utilities, such as:
 - a [`cost`](../reference/autogen/oai/completion#cost) function to calculate the cost of an API call.
 - a [`test`](../reference/autogen/oai/completion#test) function to conveniently evaluate the configuration over test data.
 - a [`extract_text`](../reference/autogen/oai/completion#extract_text) function to extract the text from a completion or chat response.
 - a [`set_cache`](../reference/autogen/oai/completion#extract_text) function to set the seed and cache path. The caching is introduced in the section above, with the benefit of cost saving, reproducibility, and controlled randomness.
 
-Interested in trying it yourself? Please check the following notebook examples:
+### Code
+
+[`flaml.autogen.code_utils`](../reference/autogen/code_utils) offers code-related utilities, such as:
+- a [`improve_code`](../reference/autogen/code_utils#improve_code) function to improve code for a given objective.
+- a [`generate_assertions`](../reference/autogen/code_utils#generate_assertions) function to generate assertion statements from function signature and docstr.
+- a [`implement`](../reference/autogen/code_utils#implement) function to implement a function from a definition.
+- a [`eval_function_completions`](../reference/autogen/code_utils#eval_function_completions) function to evaluate the success of a function completion task, or select a response from a list of responses using generated assertions.
+
+### Math
+
+[`flaml.autogen.math_utils`](../reference/autogen/math_utils) offers utilities for math problems, such as:
+- a [eval_math_responses](../reference/autogen/math_utils#eval_math_responses) function to select a response using voting, and check if the final answer is correct if the canonical solution is provided.
+
+
+*Interested in trying it yourself? Please check the following notebook examples:*
 * [Optimize for Code Gen](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_openai.ipynb)
 * [Optimize for Math](https://github.com/microsoft/FLAML/blob/main/notebook/autogen_chatgpt.ipynb)