Merge branch 'github_agent_webhooks' into 132-smart-memory-management

ai_ta_backend/agents/README.md

@@ -12,11 +12,6 @@ playwright install # for the AI to brows the web
 flask --app ai_ta_backend.main:app --debug run --port 8000
 ```
 
-```bash
-# run command w/ logging
-newrelic-admin run-program flask --app ai_ta_backend.main:app --debug run --port 8000
-```
-
 **For debugging**, to run an individual file, use this syntax to maintain proper imports and .env vars: `python -m ai_ta_backend.agents.vectordb`
 
 ## Project Management // TODOs
@@ -27,7 +22,7 @@ newrelic-admin run-program flask --app ai_ta_backend.main:app --debug run --port
 
 As a dev, request to be added here:
 * [LangSmith org](https://smith.langchain.com/o/f7abb6a0-31f6-400c-8bc1-62ade4b67dc1)
-* [NewRelic logging / observability](https://one.newrelic.com/admin-portal/organizations/users-list?account=4209060&begin=1698180809228&end=1698180929228&state=5be3d246-c297-3140-1cb9-d6cc0aa1af17)
+* Sentry.io and PostHog for python errors and logs, respectively.
 * [Github Org](https://github.com/UIUC-Chatbot)
 
 ## Overview (in progress)

ai_ta_backend/agents/code_intrepreter_sanbox.py

@@ -0,0 +1,95 @@
+import threading
+import time
+import uuid
+import termcolor
+import io
+from typing import Any, Optional, Tuple
+from e2b import CodeInterpreter, EnvVars, Sandbox
+
+class E2B_class():
+  def __init__(self, langsmith_run_id: str, env_vars: Optional[EnvVars] = None):
+    '''
+    # TODOs:
+    1. Maybe `git clone` the repo to a temp folder and run the code there
+    2. On agent finish, delete sandbox
+    '''
+
+    self.langsmith_run_id = langsmith_run_id
+    self.sandbox = Sandbox(env_vars=env_vars)
+    self.sandboxID = self.sandbox.id
+    self.sandbox.keep_alive(60 * 60 * 1) # 1 hour max
+    self.command_timeout = 3 * 60 # 3 minutes
+    self.existing_files = []
+    self.working_dir = '/home/user/'
+    self.curr_terminal_output = ''
+    # self.install_base_packages()
+
+  def install_base_packages(self):
+    self.run_shell("pip install -U numpy pandas matplotlib seaborn scikit-learn scipy")
+
+  def delete_sandbox(self):
+    self.sandbox.close()
+
+  def run_python_code(self, code: str):
+    print(termcolor.colored(f"IN SHELL RUN PYTHON CODE...\n{code}", 'blue', attrs=['bold']))
+
+    code_file = io.StringIO(code)
+    code_file.name = f"{uuid.uuid4}_code.py"
+    filepath = self.sandbox.upload_file(code_file)
+
+    shell_output = self.run_shell(f"python {filepath}")
+    return shell_output
+
+  def run_shell(self, shell_command: str):
+    print(termcolor.colored(f"IN SHELL EXECUTION with command: {shell_command}", 'blue', attrs=['bold']))
+    self.curr_terminal_output = ''
+
+    def on_exit():
+      """Block until the command has exited"""
+      self.exit_event.set()  # Signal that the command has exited
+
+    start_time = time.monotonic()
+    self.exit_event = threading.Event()
+    self.sandbox.terminal.start(cmd=shell_command, cwd=self.working_dir, on_data=self.handle_terminal_on_data, cols=120, rows=80, on_exit=on_exit, timeout=self.command_timeout)
+
+    self.exit_event.wait()  # Block until on_exit is called
+    print(termcolor.colored(f"＄ Shell execution complete, Runtime: {(time.monotonic() - start_time):.2f} seconds", 'blue', attrs=['bold']))
+    return self.curr_terminal_output
+
+  def handle_terminal_on_data(self, data: str):
+    self.curr_terminal_output+=str(data)
+    print(termcolor.colored(data, 'yellow', attrs=['bold']))
+
+def EXPERIMENTAL_run_simple_notebook(code, cwd: str = "", timeout: Optional[int] = None, env_vars: Optional[EnvVars] = None) -> Tuple[str, str, list[Any]]:
+  """
+
+  TBD if this is helpful; the one thing it uniquely does is grab matplotlib outputs. Simply, plt.show() becomes an "artifact" that can be downloaded.
+
+  Args:
+      code (_type_): _description_
+      timeout (Optional[int], optional): _description_. Defaults to None.
+      cwd (Optional[str], optional): _description_. Defaults to "".
+      env_vars (Optional[EnvVars], optional): _description_. Defaults to None.
+
+  Returns:
+      Tuple[str, str, list[Any]]: _description_
+  """
+
+  # Don't use code intrepreter -- super limited, no shell access.
+  # sandbox = Sandbox(env_vars={"FOO": "Hello"})
+  sandbox = CodeInterpreter(env_vars={"FOO": "Hello"})
+
+  # sandbox.install_python_packages('ffmpeg')
+  # sandbox.install_system_packages('ffmpeg')
+  # with open("path/to/local/file", "rb") as f:
+  #   remote_path = sandbox.upload_file(f)  
+
+  stdout, stderr, artifacts = sandbox.run_python(code, timeout=timeout, cwd=cwd, env_vars=env_vars)
+
+  artifact_files = []
+  for artifact in artifacts:
+    # Now you can save this file, send it to frontend, or anything else
+    artifact_files.append(artifact.download())
+
+  sandbox.close()
+  return stdout, stderr, artifact_files