Josephrp patch 2 #3
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removed gradio (didnt work)
focus here
always push on instinct
… Kernel plugin.
# Aider chat conversation:
USER: hey aider -- got a plan in place to create a semantic kernel plugin that makes use of the gorilla cli. I added the convo with our lead to your chat window, let's get started on implementation.
ASSISTANT: Great! Let's start with the implementation.
The first step is to create a new Python file for our Semantic Kernel plugin. Let's call it `gorilla_plugin.py`.
Here's the initial code for `gorilla_plugin.py`:
```python
# Import necessary libraries
# Assuming Gorilla CLI and Semantic Kernel SDK are installed and accessible
def process_user_input(input_description):
"""
Process the high-level user input to a format suitable for Gorilla CLI.
(This function might involve natural language processing or simple parsing)
"""
# Process input
processed_input = input_description # Simplified for illustration
return processed_input
def generate_cli_commands(processed_input):
"""
Use Gorilla CLI to generate a series of CLI commands based on the processed input.
(This function would interface with Gorilla CLI)
"""
# For the sake of example, let's assume we have a function in Gorilla CLI that does this.
# In reality, you would replace this with actual Gorilla CLI function calls.
cli_commands = ["echo 'Command 1'", "echo 'Command 2'"] # Example commands
return cli_commands
def execute_commands(cli_commands):
"""
Execute the generated CLI commands and provide an interface for user feedback.
"""
for command in cli_commands:
# Execute the command (this could be done via a subprocess call in a real scenario)
print(f"Executing: {command}")
# Here you would insert the actual command execution logic
def main():
# Example user input
user_input = "Generate a report from yesterday's logs and email it to the team"
# Process the input
processed_input = process_user_input(user_input)
# Generate CLI commands
cli_commands = generate_cli_commands(processed_input)
# Execute commands and handle feedback
execute_commands(cli_commands)
if __name__ == "__main__":
main()
```
This script is a basic outline. The actual implementation would involve more sophisticated input processing and integration with Gorilla CLI. You can start by plugging in your existing Semantic Kernel plugins and Gorilla CLI functionalities into the respective placeholder functions.
As you progress, keep refining each part based on the feedback and results from your initial tests.
USER: great, sounds good
ASSISTANT: Fantastic! If you have any questions or need further assistance as you begin implementing and testing the code, don't hesitate to ask. Happy coding!
USER: let's start with file creation and implementation.
USER: great, let's get started implementing.
ASSISTANT: Sure, to get started with the implementation, we need to create a new file `gorilla_plugin.py` and add the initial code to it.
Here is the *SEARCH/REPLACE block* for creating the new file:
gorilla_plugin.py
```python
<<<<<<< SEARCH
=======
# Import necessary libraries
# Assuming Gorilla CLI and Semantic Kernel SDK are installed and accessible
def process_user_input(input_description):
"""
Process the high-level user input to a format suitable for Gorilla CLI.
(This function might involve natural language processing or simple parsing)
"""
# Process input
processed_input = input_description # Simplified for illustration
return processed_input
def generate_cli_commands(processed_input):
"""
Use Gorilla CLI to generate a series of CLI commands based on the processed input.
(This function would interface with Gorilla CLI)
"""
# For the sake of example, let's assume we have a function in Gorilla CLI that does this.
# In reality, you would replace this with actual Gorilla CLI function calls.
cli_commands = ["echo 'Command 1'", "echo 'Command 2'"] # Example commands
return cli_commands
def execute_commands(cli_commands):
"""
Execute the generated CLI commands and provide an interface for user feedback.
"""
for command in cli_commands:
# Execute the command (this could be done via a subprocess call in a real scenario)
print(f"Executing: {command}")
# Here you would insert the actual command execution logic
def main():
# Example user input
user_input = "Generate a report from yesterday's logs and email it to the team"
# Process the input
processed_input = process_user_input(user_input)
# Generate CLI commands
cli_commands = generate_cli_commands(processed_input)
# Execute commands and handle feedback
execute_commands(cli_commands)
if __name__ == "__main__":
main()
>>>>>>> REPLACE
```
This is a basic outline of the plugin. You will need to replace the placeholder functions with the actual implementation that interfaces with the Gorilla CLI and your Semantic Kernel plugins.
…of processing user input, generating CLI commands, and executing commands using the Gorilla CLI.
# Aider chat conversation:
USER: i've got a skeleton implementation of gorilla_plugin.py written there for you, let's go ahead and start implementing the outlined functionality.
ASSISTANT: Sure, I can help with that. However, I need more specific instructions on what changes you want to make to the `gorilla_plugin.py` file. Could you please provide more details? For example, do you want to implement real functionality for the `process_user_input`, `generate_cli_commands`, and `execute_commands` functions? If so, what should these functions do exactly?
USER: let's model the functionality off of `plugins/bing_connector.py`, allowing for input from a user or agent in the form of a natural language query or high level overview of the user's problem and assisting them with the process of performing a series of executions to accomplish their goal.
ASSISTANT: To implement the functionality in `gorilla_plugin.py` similar to `bing_connector.py`, we need to:
1. Define a class `GorillaPlugin` that will encapsulate the functionality.
2. Add an `__init__` method to initialize the plugin.
3. Implement a `process_user_input` method that takes a natural language query and processes it.
4. Implement a `generate_cli_commands` method that generates CLI commands based on the processed input.
5. Implement an `execute_commands` method that executes the generated CLI commands.
Here are the *SEARCH/REPLACE* blocks:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# Import necessary libraries
# Assuming Gorilla CLI and Semantic Kernel SDK are installed and accessible
def process_user_input(input_description):
"""
Process the high-level user input to a format suitable for Gorilla CLI.
(This function might involve natural language processing or simple parsing)
"""
# Process input
processed_input = input_description # Simplified for illustration
return processed_input
def generate_cli_commands(processed_input):
"""
Use Gorilla CLI to generate a series of CLI commands based on the processed input.
(This function would interface with Gorilla CLI)
"""
# For the sake of example, let's assume we have a function in Gorilla CLI that does this.
# In reality, you would replace this with actual Gorilla CLI function calls.
cli_commands = ["echo 'Command 1'", "echo 'Command 2'"] # Example commands
return cli_commands
def execute_commands(cli_commands):
"""
Execute the generated CLI commands and provide an interface for user feedback.
"""
for command in cli_commands:
# Execute the command (this could be done via a subprocess call in a real scenario)
print(f"Executing: {command}")
# Here you would insert the actual command execution logic
def main():
# Example user input
user_input = "Generate a report from yesterday's logs and email it to the team"
# Process the input
processed_input = process_user_input(user_input)
# Generate CLI commands
cli_commands = generate_cli_commands(processed_input)
# Execute commands and handle feedback
execute_commands(cli_commands)
if __name__ == "__main__":
main()
=======
class GorillaPlugin:
"""
A plugin that uses the Gorilla CLI to perform a series of executions based on a natural language query or high level overview of the user's problem.
"""
def __init__(self):
pass
def process_user_input(self, input_description: str) -> str:
"""
Process the high-level user input to a format suitable for Gorilla CLI.
(This function might involve natural language processing or simple parsing)
"""
# Process input
processed_input = input_description # Simplified for illustration
return processed_input
def generate_cli_commands(self, processed_input: str) -> list:
"""
Use Gorilla CLI to generate a series of CLI commands based on the processed input.
(This function would interface with Gorilla CLI)
"""
# For the sake of example, let's assume we have a function in Gorilla CLI that does this.
# In reality, you would replace this with actual Gorilla CLI function calls.
cli_commands = ["echo 'Command 1'", "echo 'Command 2'"] # Example commands
return cli_commands
def execute_commands(self, cli_commands: list):
"""
Execute the generated CLI commands and provide an interface for user feedback.
"""
for command in cli_commands:
# Execute the command (this could be done via a subprocess call in a real scenario)
print(f"Executing: {command}")
# Here you would insert the actual command execution logic
def main():
# Example user input
user_input = "Generate a report from yesterday's logs and email it to the team"
# Initialize GorillaPlugin
gorilla_plugin = GorillaPlugin()
# Process the input
processed_input = gorilla_plugin.process_user_input(user_input)
# Generate CLI commands
cli_commands = gorilla_plugin.generate_cli_commands(processed_input)
# Execute commands and handle feedback
gorilla_plugin.execute_commands(cli_commands)
if __name__ == "__main__":
main()
>>>>>>> REPLACE
```
Please note that the actual implementation of the `process_user_input`, `generate_cli_commands`, and `execute_commands` methods will depend on the specifics of the Gorilla CLI and the Semantic Kernel SDK. The provided code is a skeleton that needs to be filled with the actual logic.
…Connector` plugin, we will need to make several changes to the `plugins/gorilla_plugin.py` file. We will add asynchronous methods to interact with the Gorilla CLI, and we will implement an iterative feedback loop where the user can provide confirmation before each execution and feedback about the environment afterwards.
# Aider chat conversation:
USER: `plugins/gorilla_plugin.py` `plugins/bing_connector.py` i want to build out the gorilla plugin in a similar way to the bing connector plugin, in order to enable the usage of the gorilla cli to assist with command line executions that can be performed in series to accomplish the user's high level goal with an iterative feedback loop so the user can provide confirmation before each execution and feedback about the environment afterwards.
ASSISTANT: To build out the `GorillaPlugin` in a similar way to the `BingConnector` plugin, we will need to make several changes to the `plugins/gorilla_plugin.py` file. We will add asynchronous methods to interact with the Gorilla CLI, and we will implement an iterative feedback loop where the user can provide confirmation before each execution and feedback about the environment afterwards.
Here are the steps we need to take:
1. Add necessary imports for asynchronous operations and typing.
2. Add an asynchronous method to interact with the Gorilla CLI.
3. Add an asynchronous method to execute commands with user confirmation and feedback.
4. Modify the `main()` function to support asynchronous execution.
Let's start with the changes:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
class GorillaPlugin:
=======
import asyncio
from typing import List
class GorillaPlugin:
_cli_path: str
def __init__(self, cli_path: str):
self._cli_path = cli_path
>>>>>>> REPLACE
```
This block adds the necessary imports for asynchronous operations and a constructor parameter for the path to the Gorilla CLI.
Next, we'll add the asynchronous method to interact with the Gorilla CLI:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
def execute_commands(self, cli_commands: list):
"""
Execute the generated CLI commands and provide an interface for user feedback.
"""
for command in cli_commands:
# Execute the command (this could be done via a subprocess call in a real scenario)
print(f"Executing: {command}")
# Here you would insert the actual command execution logic
=======
async def execute_commands(self, cli_commands: List[str]):
"""
Execute the generated CLI commands and provide an interface for user feedback.
"""
for command in cli_commands:
user_confirmation = input(f"Do you want to execute: {command}? (yes/no) ")
if user_confirmation.lower() == 'yes':
# Here you would insert the actual command execution logic, e.g., subprocess call
print(f"Executing: {command}")
await asyncio.sleep(0) # Simulate async execution
# After execution, get user feedback
user_feedback = input("Please provide feedback about the environment: ")
print(f"Received feedback: {user_feedback}")
else:
print("Command execution skipped by user.")
>>>>>>> REPLACE
```
This block replaces the synchronous `execute_commands` method with an asynchronous one that asks for user confirmation before executing each command and collects user feedback after each execution.
Finally, we need to modify the `main()` function to support asynchronous execution:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
def main():
# Example user input
user_input = "Generate a report from yesterday's logs and email it to the team"
# Initialize GorillaPlugin
gorilla_plugin = GorillaPlugin()
# Process the input
processed_input = gorilla_plugin.process_user_input(user_input)
# Generate CLI commands
cli_commands = gorilla_plugin.generate_cli_commands(processed_input)
# Execute commands and handle feedback
gorilla_plugin.execute_commands(cli_commands)
if __name__ == "__main__":
main()
=======
async def main():
# Example user input
user_input = "Generate a report from yesterday's logs and email it to the team"
# Initialize GorillaPlugin with the path to the Gorilla CLI
gorilla_plugin = GorillaPlugin(cli_path="/path/to/gorilla-cli")
# Process the input
processed_input = gorilla_plugin.process_user_input(user_input)
# Generate CLI commands
cli_commands = gorilla_plugin.generate_cli_commands(processed_input)
# Execute commands and handle feedback
await gorilla_plugin.execute_commands(cli_commands)
if __name__ == "__main__":
asyncio.run(main())
>>>>>>> REPLACE
```
This block changes the `main()` function to an asynchronous function and uses `asyncio.run()` to execute it, which is necessary to run the asynchronous `execute_commands` method.
These changes will allow the `GorillaPlugin` to interact with the Gorilla CLI asynchronously and provide an iterative feedback loop for command execution. Please note that the actual implementation of the Gorilla CLI interaction (e.g., subprocess calls) is not included and should be implemented according to the specifics of the Gorilla CLI.
…ocess calls in `GorillaPlugin`.
# Aider chat conversation:
USER: i've added the gorilla cli to the environment and modified the requirements accordingly. let's get started with the subprocess calls in accordance with the gorilla cli spec.
ASSISTANT: To integrate the Gorilla CLI with the `GorillaPlugin` and replace the placeholder command execution with actual subprocess calls, we need to modify the `plugins/gorilla_plugin.py` file. We will use the `asyncio.create_subprocess_shell` function to execute the commands asynchronously and capture their output.
Here are the steps and the corresponding *SEARCH/REPLACE* blocks:
1. Import the `asyncio.subprocess` module to use for subprocess calls.
2. Replace the placeholder command execution logic with actual subprocess calls using `asyncio.create_subprocess_shell`.
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
import asyncio
from typing import List
=======
import asyncio
import asyncio.subprocess as subprocess
from typing import List
>>>>>>> REPLACE
```
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# Here you would insert the actual command execution logic, e.g., subprocess call
print(f"Executing: {command}")
await asyncio.sleep(0) # Simulate async execution
# After execution, get user feedback
user_feedback = input("Please provide feedback about the environment: ")
print(f"Received feedback: {user_feedback}")
=======
# Execute the command using subprocess
process = await subprocess.create_subprocess_shell(
command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
print(f"Command executed successfully: {command}")
print(f"Output: {stdout.decode().strip()}")
else:
print(f"Command failed: {command}")
print(f"Error: {stderr.decode().strip()}")
# After execution, get user feedback
user_feedback = input("Please provide feedback about the environment: ")
print(f"Received feedback: {user_feedback}")
>>>>>>> REPLACE
```
These changes will allow the `GorillaPlugin` to execute CLI commands asynchronously and handle their output appropriately. The user will still be prompted for confirmation before each command execution and for feedback after each command is run.
…fore and after executing commands.
# Aider chat conversation:
USER: hmm, is there a way we can direct the plugin to collect information about the environment the commands are being run in, so it doesn't need to always/only rely on input from the user?
ASSISTANT: Certainly! To collect information about the environment in which the commands are being run, we can enhance the `GorillaPlugin` to gather system information before and after executing commands. This can be done by executing additional commands that retrieve system information, such as `uname` on Unix-like systems or `systeminfo` on Windows.
Here's how we can modify the `plugins/gorilla_plugin.py` file to include environment data collection:
1. Add a method to gather system information.
2. Call this method before and after executing the CLI commands to collect the environment information.
Let's implement these changes:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
import asyncio
import asyncio.subprocess as subprocess
from typing import List
=======
import asyncio
import asyncio.subprocess as subprocess
from typing import List, Dict
>>>>>>> REPLACE
```
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
class GorillaPlugin:
_cli_path: str
def __init__(self, cli_path: str):
self._cli_path = cli_path
=======
class GorillaPlugin:
_cli_path: str
_env_info: Dict[str, str]
def __init__(self, cli_path: str):
self._cli_path = cli_path
self._env_info = {}
>>>>>>> REPLACE
```
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
async def execute_commands(self, cli_commands: List[str]):
"""
Execute the generated CLI commands and provide an interface for user feedback.
"""
=======
async def collect_environment_info(self) -> None:
"""
Collects information about the environment where the commands are executed.
"""
uname_command = "uname -a" # This is for Unix-like systems, for Windows use 'systeminfo'
process = await subprocess.create_subprocess_shell(
uname_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
self._env_info['uname'] = stdout.decode().strip()
else:
self._env_info['uname'] = f"Error collecting environment info: {stderr.decode().strip()}"
async def execute_commands(self, cli_commands: List[str]):
# Collect initial environment info
await self.collect_environment_info()
initial_env_info = self._env_info.copy()
>>>>>>> REPLACE
```
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# After execution, get user feedback
user_feedback = input("Please provide feedback about the environment: ")
print(f"Received feedback: {user_feedback}")
=======
# Collect updated environment info
await self.collect_environment_info()
updated_env_info = self._env_info.copy()
# Compare initial and updated environment info, if needed
# ...
# After execution, get user feedback
user_feedback = input("Please provide feedback about the environment: ")
print(f"Received feedback: {user_feedback}")
print(f"Initial environment info: {initial_env_info}")
print(f"Updated environment info: {updated_env_info}")
>>>>>>> REPLACE
```
These changes will allow the `GorillaPlugin` to collect system information before and after executing each command. The `collect_environment_info` method can be expanded to collect more detailed information as required. The initial and updated environment information can be compared to detect any changes that occurred as a result of the command execution.
…ds from `plugins/gorilla_plugin.py`.
# Aider chat conversation:
USER: cool. let's proceed with the implementation of the remaining unimplemented functions.
ASSISTANT: To proceed with the implementation of the remaining unimplemented functions, I need to know which specific functions you are referring to. The provided `read-write` files are `plugins/bing_connector.py` and `plugins/gorilla_plugin.py`. Both of these files contain classes with methods that seem to be implemented.
If you are referring to additional functionality or enhancements to these classes, please specify what changes or new functions you would like to implement. If there are other files or functions outside of these that you want to modify or implement, please provide the details or add the relevant files to the chat if they are not already `read-write`.
USER: `process_user_input` and `generate_cli_commands` appear to be placeholder functions as far as I can tell, unless you rolled those into the refactor? If so, then we can go ahead and delete themso we're not cluttering up the plugin.
ASSISTANT: Understood. If `process_user_input` and `generate_cli_commands` are placeholder functions and do not contain the actual implementation for processing user input or generating CLI commands, we can remove them from the `plugins/gorilla_plugin.py` file to reduce clutter.
Here are the *SEARCH/REPLACE* blocks to remove the `process_user_input` and `generate_cli_commands` methods:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
def process_user_input(self, input_description: str) -> str:
"""
Process the high-level user input to a format suitable for Gorilla CLI.
(This function might involve natural language processing or simple parsing)
"""
# Process input
processed_input = input_description # Simplified for illustration
return processed_input
def generate_cli_commands(self, processed_input: str) -> list:
"""
Use Gorilla CLI to generate a series of CLI commands based on the processed input.
(This function would interface with Gorilla CLI)
"""
# For the sake of example, let's assume we have a function in Gorilla CLI that does this.
# In reality, you would replace this with actual Gorilla CLI function calls.
cli_commands = ["echo 'Command 1'", "echo 'Command 2'"] # Example commands
return cli_commands
=======
>>>>>>> REPLACE
```
Please confirm if you would like to proceed with these changes.
…ain function.
# Aider chat conversation:
USER: awesome. Does our `GorillaPlugin` and `gorilla_plugin`
ASSISTANT: It appears that there is a discrepancy in the naming of the `GorillaPlugin` class in the `plugins/gorilla_plugin.py` file. The class is defined without any methods named `process_user_input` or `generate_cli_commands`, which you mentioned earlier as being placeholders. Since these methods are not present in the provided code, there is no need to remove them.
However, if you are referring to the `main` function within the `gorilla_plugin.py` file, which contains calls to `process_user_input` and `generate_cli_commands`, we can remove the calls to these non-existent methods to clean up the code.
Here is the *SEARCH/REPLACE* block to remove the calls
^C KeyboardInterrupt
USER: sorry for the interruption, let's proceed with cleaning up `gorilla_plugin` and `GorillaPlugin` in the appropriate file
ASSISTANT: No problem. To clean up the `gorilla_plugin.py` file, we will remove the placeholder calls to `process_user_input` and `generate_cli_commands` from the `main` function since these methods do not exist in the `GorillaPlugin` class.
Here are the *SEARCH/REPLACE* blocks to remove the calls to the non-existent methods:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# Process the input
processed_input = gorilla_plugin.process_user_input(user_input)
# Generate CLI commands
cli_commands = gorilla_plugin.generate_cli_commands(processed_input)
=======
# Placeholder for processing the input and generating CLI commands
# TODO: Implement the logic to process input and generate CLI commands
cli_commands = ["echo 'Placeholder command'"]
>>>>>>> REPLACE
```
This change replaces the calls to the non-existent methods with a placeholder comment and a sample command for demonstration purposes. Please confirm if you would like to proceed with this change or provide further instructions.
…er feedback and command execution logic in `gorilla_plugin.py`.
# Aider chat conversation:
USER: great. now, do we need any additional refactors or modifications to the codebase in order to make sure the `gorilla_plugin` file works as expected, or are we finished up?
ASSISTANT: Based on the provided `gorilla_plugin.py` file, it seems that the main functionality for executing commands and collecting environment information is implemented. However, there are a few considerations and potential improvements that could be made to ensure the plugin works as expected:
1. **Error Handling**: The current implementation of `execute_commands` does not handle exceptions that may occur during the execution of subprocesses or when collecting environment information. It would be beneficial to add try-except blocks to handle potential exceptions gracefully.
2. **Command Generation**: The `main` function contains a placeholder for generating CLI commands based on user input. This indicates that the logic for interpreting user input and generating corresponding commands is not yet implemented.
3. **User Confirmation**: The `execute_commands` method currently uses `input` to get user confirmation before executing each command. This synchronous call to `input` might not be suitable for an asynchronous context. Consider designing a different mechanism for user confirmation that fits the asynchronous nature of the plugin.
4. **Logging**: The plugin uses print statements for output. Depending on the use case, it might be more appropriate to use a logging framework that can be configured for different levels of verbosity and can write to files or other outputs.
5. **Testing**: There are no tests provided. Writing unit tests for the plugin's functionality would be a good practice to ensure that it works as expected and to prevent regressions in the future.
6. **Configuration**: The `GorillaPlugin` class is initialized with a `cli_path` argument, but it is not used anywhere in the class. If the Gorilla CLI path is necessary for the plugin's operation, the code should be updated to use it.
7. **Environment Information Comparison**: The comment `# Compare initial and updated environment info, if needed` suggests that there is a placeholder for comparing the environment before and after command execution. This comparison logic needs to be implemented if it is required for the plugin's functionality.
If you would like to proceed with any of these improvements, please specify which ones, and I can provide the necessary *SEARCH/REPLACE* blocks or additional code snippets. If the plugin is considered complete for your current needs, then no further action is required.
USER: We do need the comparison logic. Error handling would be wise, and we do need to implement the user feedback and command execution logic as well, those are pretty core to the functionlaity of the plugin. right now the codebase doesn't include any logging or tests, so we can worry about those later, i think. i'm not sure whether the cli_path argument is unnecessary or not, but we should figure that out and resolve it accordingly.
ASSISTANT: To address the points you've mentioned, we'll need to make several modifications to the `gorilla_plugin.py` file:
1. **Implement Comparison Logic**: Add a method to compare the initial and updated environment information and report any changes.
2. **Improve Error Handling**: Add try-except blocks around subprocess execution and environment collection to handle exceptions.
3. **Implement User Feedback and Command Execution Logic**: Replace the placeholder logic with actual implementation for processing user input and generating CLI commands.
4. **Resolve `cli_path` Usage**: Determine if `cli_path` is needed and implement its usage or remove it if unnecessary.
Let's start with the first task: Implementing the comparison logic.
Here is the *SEARCH/REPLACE* block to add a method for comparing environment information:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
async def execute_commands(self, cli_commands: List[str]):
# Collect initial environment info
await self.collect_environment_info()
initial_env_info = self._env_info.copy()
=======
def compare_environment_info(self, initial_env_info: Dict[str, str], updated_env_info: Dict[str, str]) -> Dict[str, str]:
"""
Compares the initial and updated environment information and returns the differences.
"""
changes = {}
for key in initial_env_info:
if initial_env_info[key] != updated_env_info.get(key, None):
changes[key] = {
'initial': initial_env_info[key],
'updated': updated_env_info.get(key, None)
}
return changes
async def execute_commands(self, cli_commands: List[str]):
# Collect initial environment info
await self.collect_environment_info()
initial_env_info = self._env_info.copy()
# ... (rest of the method remains unchanged)
>>>>>>> REPLACE
```
Next, we'll add error handling to the `execute_commands` and `collect_environment_info` methods.
Here is the *SEARCH/REPLACE* block to add error handling:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
process = await subprocess.create_subprocess_shell(
uname_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
self._env_info['uname'] = stdout.decode().strip()
else:
self._env_info['uname'] = f"Error collecting environment info: {stderr.decode().strip()}"
=======
try:
process = await subprocess.create_subprocess_shell(
uname_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
self._env_info['uname'] = stdout.decode().strip()
else:
self._env_info['uname'] = f"Error collecting environment info: {stderr.decode().strip()}"
except Exception as e:
self._env_info['uname'] = f"Exception collecting environment info: {str(e)}"
>>>>>>> REPLACE
```
For the `execute_commands` method, you would add similar try-except blocks around the subprocess calls.
Now, let's address the `cli_path` argument. If the Gorilla CLI is a command-line interface that needs to be called, then `cli_path` should be used to construct the command that is executed. If it's not needed, it should be removed from the class constructor.
Since I don't have the context on how the Gorilla CLI is used, I'll need your input on whether to implement or remove the `cli_path` argument.
Lastly, for the user feedback and command execution logic, we need to replace the placeholder logic with actual implementation. This would involve designing the logic to interpret user input and generate CLI commands, which seems to be a complex task and might require a detailed understanding of the requirements and the Gorilla CLI's capabilities.
Please let me know how you would like to proceed with the `cli_path` and the user feedback/command execution logic, and I can provide further assistance.
…lla CLI, ask for user confirmation before executing each command, and provide feedback after execution.
# Aider chat conversation:
USER: the gorilla CLI is in fact a command line interface, yes. please provide the search-replace blocks for the `execute_commands` method if you didn't already. The gorilla cli is a command line interface designed to take a natural language query and return a corresponding cli execution based on the llm's it has access to. for instance, the normal usage of the gorilla cli could be 'gorilla "generate 100 random characters into a file called test.txt"' and return 'welcome to Gorilla. Use arrows to select >>cat /dev/urandom | env LC_ALL=C...' etc etc. this should be enough information to implement the desired logic and modifications accordingly.
ASSISTANT: To implement the `execute_commands` method in the `gorilla_plugin.py` file, we need to ensure that the method can take a list of natural language commands, pass them to the Gorilla CLI, and then execute the resulting CLI commands. We will also need to handle user confirmation before executing each command and provide feedback after execution.
Here are the *SEARCH/REPLACE* blocks for the `execute_commands` method in `gorilla_plugin.py`:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
async def execute_commands(self, cli_commands: List[str]):
# Collect initial environment info
await self.collect_environment_info()
initial_env_info = self._env_info.copy()
# ... (rest of the method remains unchanged)
for command in cli_commands:
user_confirmation = input(f"Do you want to execute: {command}? (yes/no) ")
if user_confirmation.lower() == 'yes':
# Execute the command using subprocess
process = await subprocess.create_subprocess_shell(
command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
print(f"Command executed successfully: {command}")
print(f"Output: {stdout.decode().strip()}")
else:
print(f"Command failed: {command}")
print(f"Error: {stderr.decode().strip()}")
# Collect updated environment info
await self.collect_environment_info()
updated_env_info = self._env_info.copy()
# Compare initial and updated environment info, if needed
# ...
# After execution, get user feedback
user_feedback = input("Please provide feedback about the environment: ")
print(f"Received feedback: {user_feedback}")
print(f"Initial environment info: {initial_env_info}")
print(f"Updated environment info: {updated_env_info}")
else:
print("Command execution skipped by user.")
=======
async def execute_commands(self, natural_language_commands: List[str]):
# Collect initial environment info
await self.collect_environment_info()
initial_env_info = self._env_info.copy()
for nl_command in natural_language_commands:
# Pass the natural language command to the Gorilla CLI and get the CLI command
process = await subprocess.create_subprocess_shell(
f"{self._cli_path} \"{nl_command}\"",
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode != 0:
print(f"Failed to get CLI command for: {nl_command}")
print(f"Error: {stderr.decode().strip()}")
continue
cli_command = stdout.decode().strip()
print(f"CLI command to execute: {cli_command}")
# Ask for user confirmation before executing the CLI command
user_confirmation = input(f"Do you want to execute: {cli_command}? (yes/no) ")
if user_confirmation.lower() == 'yes':
# Execute the CLI command using subprocess
process = await subprocess.create_subprocess_shell(
cli_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
print(f"Command executed successfully: {cli_command}")
print(f"Output: {stdout.decode().strip()}")
else:
print(f"Command failed: {cli_command}")
print(f"Error: {stderr.decode().strip()}")
# Collect updated environment info
await self.collect_environment_info()
updated_env_info = self._env_info.copy()
# Compare initial and updated environment info
env_changes = self.compare_environment_info(initial_env_info, updated_env_info)
if env_changes:
print("Environment changes detected:")
for key, change in env_changes.items():
print(f"{key}: from '{change['initial']}' to '{change['updated']}'")
# After execution, get user feedback
user_feedback = input("Please provide feedback about the execution: ")
print(f"Received feedback: {user_feedback}")
else:
print("Command execution skipped by user.")
>>>>>>> REPLACE
```
This block replaces the placeholder logic in the `execute_commands` method with the actual logic to interact with the Gorilla CLI. It first collects the initial environment information, then for each natural language command, it gets the corresponding CLI command from the Gorilla CLI, asks the user for confirmation, and upon confirmation, executes the command. After execution, it collects the updated environment information, compares it with the initial information, and asks the user for feedback.
… to not get interrupted by the user feedback loop, cleaned up the extra init, fixed up the docstrings, changed the hardcoded cli path to an env variable, and added error handling to `execute_commands`.
# Aider chat conversation:
USER: let's cleanup and refactor gorilla_plugin.py to allow for the async interactions to not get interrupted by the user feedback loop, cleanup the extra init, fix up the docstrings, change the hardcoded cli path to an env variable, and add some error handling to `execute_commands`
ASSISTANT: To refactor `gorilla_plugin.py` according to your requirements, we will perform the following steps:
1. Remove the extra `__init__` method.
2. Update the docstrings to follow PEP 257 conventions.
3. Change the hardcoded CLI path to use an environment variable.
4. Add error handling to the `execute_commands` method.
5. Refactor the user feedback loop to ensure async interactions are not interrupted.
Let's start with the *SEARCH/REPLACE* blocks for each of these steps:
### Step 1: Remove the extra `__init__` method
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
def __init__(self, cli_path: str):
self._cli_path = cli_path
self._env_info = {}
"""
A plugin that uses the Gorilla CLI to perform a series of executions based on a natural language query or high level overview of the user's problem.
"""
def __init__(self):
pass
=======
"""
A plugin that uses the Gorilla CLI to perform a series of executions based on a natural language query or high level overview of the user's problem.
"""
>>>>>>> REPLACE
```
### Step 2: Update the docstrings
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
"""
A plugin that uses the Gorilla CLI to perform a series of executions based on a natural language query or high level overview of the user's problem.
"""
=======
"""A plugin that uses the Gorilla CLI to perform a series of executions based on a natural language query or high level overview of the user's problem."""
>>>>>>> REPLACE
```
### Step 3: Change the hardcoded CLI path to use an environment variable
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
gorilla_plugin = GorillaPlugin(cli_path="/path/to/gorilla-cli")
=======
import os
gorilla_plugin = GorillaPlugin(cli_path=os.getenv('GORILLA_CLI_PATH'))
>>>>>>> REPLACE
```
### Step 4: Add error handling to the `execute_commands` method
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
process = await subprocess.create_subprocess_shell(
f"{self._cli_path} \"{nl_command}\"",
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode != 0:
print(f"Failed to get CLI command for: {nl_command}")
print(f"Error: {stderr.decode().strip()}")
continue
=======
try:
process = await subprocess.create_subprocess_shell(
f"{self._cli_path} \"{nl_command}\"",
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode != 0:
print(f"Failed to get CLI command for: {nl_command}")
print(f"Error: {stderr.decode().strip()}")
continue
except Exception as e:
print(f"Exception while processing command '{nl_command}': {str(e)}")
continue
>>>>>>> REPLACE
```
### Step 5: Refactor the user feedback loop
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# Ask for user confirmation before executing the CLI command
user_confirmation = input(f"Do you want to execute: {cli_command}? (yes/no) ")
if user_confirmation.lower() == 'yes':
# Execute the CLI command using subprocess
process = await subprocess.create_subprocess_shell(
cli_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
print(f"Command executed successfully: {cli_command}")
print(f"Output: {stdout.decode().strip()}")
else:
print(f"Command failed: {cli_command}")
print(f"Error: {stderr.decode().strip()}")
# Collect updated environment info
await self.collect_environment_info()
updated_env_info = self._env_info.copy()
# Compare initial and updated environment info
env_changes = self.compare_environment_info(initial_env_info, updated_env_info)
if env_changes:
print("Environment changes detected:")
for key, change in env_changes.items():
print(f"{key}: from '{change['initial']}' to '{change['updated']}'")
# After execution, get user feedback
user_feedback = input("Please provide feedback about the execution: ")
print(f"Received feedback: {user_feedback}")
else:
print("Command execution skipped by user.")
=======
# Execute the CLI command using subprocess without immediate user confirmation
# User feedback will be collected after all commands have been processed
process = await subprocess.create_subprocess_shell(
cli_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
print(f"Command executed successfully: {cli_command}")
print(f"Output: {stdout.decode().strip()}")
else:
print(f"Command failed: {cli_command}")
print(f"Error: {stderr.decode().strip()}")
# Collect updated environment info
await self.collect_environment_info()
updated_env_info = self._env_info.copy()
# Compare initial and updated environment info
env_changes = self.compare_environment_info(initial_env_info, updated_env_info)
if env_changes:
print("Environment changes detected:")
for key, change in env_changes.items():
print(f"{key}: from '{change['initial']}' to '{change['updated']}'")
>>>>>>> REPLACE
```
After all commands have been processed, you can collect user feedback in a separate method or at the end of the `main` function. This will ensure that the async operations are not interrupted by user input. If you want to implement this, please provide further instructions on how you would like the user feedback to be handled.
…cute them only after user confirmation.
# Aider chat conversation:
USER: i think probably separate method, unless the end of the main function route would be more optimal in some way? we still do want to make sure commands aren't executing in the user's shell without authorization, just don't want to interrupt anything that's still running.
ASSISTANT: To ensure that commands are not executed in the user's shell without authorization and to avoid interrupting anything that's still running, we can refactor the `execute_commands` method in `gorilla_plugin.py` to queue the commands and execute them only after user confirmation. This would involve the following steps:
1. Create a new method `queue_commands` that will store the commands in a list.
2. Modify the `execute_commands` method to only execute commands after user confirmation.
3. Add a new method `confirm_and_execute_commands` that will handle user confirmation and then call `execute_commands`.
Let's start by creating the `queue_commands` method:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
async def execute_commands(self, natural_language_commands: List[str]):
# Collect initial environment info
await self.collect_environment_info()
initial_env_info = self._env_info.copy()
=======
async def queue_commands(self, natural_language_commands: List[str]) -> List[str]:
"""
Processes natural language commands and queues them for execution after user confirmation.
"""
queued_commands = []
for nl_command in natural_language_commands:
# Pass the natural language command to the Gorilla CLI and get the CLI command
try:
process = await subprocess.create_subprocess_shell(
f"{self._cli_path} \"{nl_command}\"",
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode != 0:
print(f"Failed to get CLI command for: {nl_command}")
print(f"Error: {stderr.decode().strip()}")
continue
except Exception as e:
print(f"Exception while processing command '{nl_command}': {str(e)}")
continue
cli_command = stdout.decode().strip()
queued_commands.append(cli_command)
return queued_commands
>>>>>>> REPLACE
```
Next, we will modify the `execute_commands` method to execute only after user confirmation:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# Execute the CLI command using subprocess without immediate user confirmation
# User feedback will be collected after all commands have been processed
process = await subprocess.create_subprocess_shell(
cli_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
print(f"Command executed successfully: {cli_command}")
print(f"Output: {stdout.decode().strip()}")
else:
print(f"Command failed: {cli_command}")
print(f"Error: {stderr.decode().strip()}")
# Collect updated environment info
await self.collect_environment_info()
updated_env_info = self._env_info.copy()
# Compare initial and updated environment info
env_changes = self.compare_environment_info(initial_env_info, updated_env_info)
if env_changes:
print("Environment changes detected:")
for key, change in env_changes.items():
print(f"{key}: from '{change['initial']}' to '{change['updated']}'")
=======
async def execute_commands(self, cli_commands: List[str]):
"""
Executes a list of CLI commands after user confirmation.
"""
# TODO: Implement user confirmation logic here
# Collect initial environment info
await self.collect_environment_info()
initial_env_info = self._env_info.copy()
for cli_command in cli_commands:
# Execute the CLI command using subprocess
process = await subprocess.create_subprocess_shell(
cli_command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode == 0:
print(f"Command executed successfully: {cli_command}")
print(f"Output: {stdout.decode().strip()}")
else:
print(f"Command failed: {cli_command}")
print(f"Error: {stderr.decode().strip()}")
# Collect updated environment info
await self.collect_environment_info()
updated_env_info = self._env_info.copy()
# Compare initial and updated environment info
env_changes = self.compare_environment_info(initial_env_info, updated_env_info)
if env_changes:
print("Environment changes detected:")
for key, change in env_changes.items():
print(f"{key}: from '{change['initial']}' to '{change['updated']}'")
>>>>>>> REPLACE
```
Finally, we add the `confirm_and_execute_commands` method to handle user confirmation:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
async def main():
# Example user input
user_input = "Generate a report from yesterday's logs and email it to the team"
# Initialize GorillaPlugin with the path to the Gorilla CLI
import os
gorilla_plugin = GorillaPlugin(cli_path=os.getenv('GORILLA_CLI_PATH'))
# Placeholder for processing the input and generating CLI commands
# TODO: Implement the logic to process input and generate CLI commands
cli_commands = ["echo 'Placeholder command'"]
# Execute commands and handle feedback
await gorilla_plugin.execute_commands(cli_commands)
=======
async def confirm_and_execute_commands(gorilla_plugin: GorillaPlugin, queued_commands: List[str]):
"""
Confirms with the user before executing queued commands.
"""
# TODO: Implement user confirmation logic here
# If confirmed, execute the commands
await gorilla_plugin.execute_commands(queued_commands)
async def main():
# Example user input
user_input = "Generate a report from yesterday's logs and email it to the team"
# Initialize GorillaPlugin with the path to the Gorilla CLI
import os
gorilla_plugin = GorillaPlugin(cli_path=os.getenv('GORILLA_CLI_PATH'))
# Process the input and queue CLI commands
queued_commands = await gorilla_plugin.queue_commands([user_input])
# Confirm and execute commands
await confirm_and_execute_commands(gorilla_plugin, queued_commands)
>>>>>>> REPLACE
```
These changes will ensure that commands are queued and only executed after user confirmation, which can be implemented in the `TODO` sections. This approach will prevent any interruption of ongoing processes and unauthorized command execution.
…`confirm_and_execute_commands` methods.
# Aider chat conversation:
USER: great, let's complete the todos
ASSISTANT: To complete the TODOs in the `plugins/gorilla_plugin.py` file, we need to implement user confirmation logic in both the `execute_commands` and `confirm_and_execute_commands` methods. We will prompt the user for confirmation before executing the queued commands.
Here are the changes we need to make:
1. Implement user confirmation logic in `execute_commands` method.
2. Implement user confirmation logic in `confirm_and_execute_commands` function.
Let's start with the `execute_commands` method:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# TODO: Implement user confirmation logic here
=======
# Ask for user confirmation before executing commands
user_confirmation = input("Do you want to execute the queued commands? (yes/no): ")
if user_confirmation.lower() != 'yes':
print("Execution cancelled by the user.")
return
>>>>>>> REPLACE
```
Now, let's implement the user confirmation logic in the `confirm_and_execute_commands` function:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# TODO: Implement user confirmation logic here
=======
# Ask for user confirmation before executing commands
user_confirmation = input("Do you want to execute the queued commands? (yes/no): ")
if user_confirmation.lower() == 'yes':
# If confirmed, execute the commands
await gorilla_plugin.execute_commands(queued_commands)
else:
print("Execution cancelled by the user.")
>>>>>>> REPLACE
```
These changes will prompt the user for confirmation before executing any commands, ensuring that commands are not executed without authorization.
… commands in `plugins/gorilla_plugin.py`.
# Aider chat conversation:
USER: i notice we have an unreachable code block on line 72, let's fix that up
ASSISTANT: To fix the unreachable code block in `plugins/gorilla_plugin.py`, we need to remove the duplicated code that processes the natural language commands. This duplicated block is after the `return queued_commands` statement, which makes it unreachable.
Here is the *SEARCH/REPLACE* block to remove the unreachable code:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
return queued_commands
for nl_command in natural_language_commands:
# Pass the natural language command to the Gorilla CLI and get the CLI command
try:
process = await subprocess.create_subprocess_shell(
f"{self._cli_path} \"{nl_command}\"",
stdout=subprocess.PIPE,
stderr=subprocess.PIPE
)
stdout, stderr = await process.communicate()
if process.returncode != 0:
print(f"Failed to get CLI command for: {nl_command}")
print(f"Error: {stderr.decode().strip()}")
continue
except Exception as e:
print(f"Exception while processing command '{nl_command}': {str(e)}")
continue
cli_command = stdout.decode().strip()
print(f"CLI command to execute: {cli_command}")
=======
return queued_commands
>>>>>>> REPLACE
```
This change will remove the duplicated and unreachable code block, ensuring that the `queue_commands` method ends correctly after queuing the commands.
…ute_commands method.
# Aider chat conversation:
USER: in our execute_commands function, my linter's throwing an undefined name gorilla_plugin and queued_commands, does that need a fix?
ASSISTANT: Yes, the `execute_commands` function in the `plugins/gorilla_plugin.py` file contains a call to `gorilla_plugin.execute_commands(queued_commands)` which is incorrect. This line should not be there because the function is already within the `GorillaPlugin` class and should be calling `self.execute_commands(cli_commands)` instead. However, since the function is already the `execute_commands` method, this line is redundant and should be removed.
Let's fix this by removing the incorrect line from the `execute_commands` method.
Here is the *SEARCH/REPLACE* block for the necessary change:
plugins/gorilla_plugin.py
```python
<<<<<<< SEARCH
# Ask for user confirmation before executing commands
user_confirmation = input("Do you want to execute the queued commands? (yes/no): ")
if user_confirmation.lower() == 'yes':
# If confirmed, execute the commands
await gorilla_plugin.execute_commands(queued_commands)
else:
print("Execution cancelled by the user.")
=======
# Ask for user confirmation before executing commands
user_confirmation = input("Do you want to execute the queued commands? (yes/no): ")
if user_confirmation.lower() != 'yes':
print("Execution cancelled by the user.")
return
>>>>>>> REPLACE
```
This change removes the incorrect recursive call and the undefined `queued_commands` variable, and also corrects the user confirmation logic to return early if the user does not confirm execution.
…ronment variable and modified `OAI_CONFIG_LIST.json` to use environment variables for sensitive information, and updated `README.md` with instructions on setting up environment variables.
# Aider chat conversation:
USER: alright, I've made some small refactors, things look good to me. Does the file now look like it fits all of my initially stated requirements, and should behave as intended?
ASSISTANT: Based on the provided `plugins/gorilla_plugin.py` file and the previous discussions, let's review the requirements and the current state of the code to ensure it behaves as intended:
1. **User Confirmation Logic**: Both `execute_commands` and `confirm_and_execute_commands` methods should ask for user confirmation before executing commands. This logic is present and appears to be correctly implemented.
2. **Unreachable Code**: Previously, there was an issue with unreachable code that has been addressed in a past commit. The current code does not show any signs of unreachable code blocks.
3. **Undefined Names**: The issue with the undefined name `gorilla_plugin` and `queued_commands` in the `execute_commands` method has been resolved in a past commit.
4. **Environment Information Collection**: The `collect_environment_info` method is implemented to collect environment information before and after command execution.
5. **Environment Comparison**: The `compare_environment_info` method is implemented to compare the initial and updated environment information and report any changes.
6. **Command Queueing and Execution**: The `queue_commands` method processes natural language commands and queues them for execution. The `execute_commands` method executes the queued CLI commands after user confirmation.
7. **Async/Await Usage**: The methods that involve running subprocesses and collecting environment information are correctly using `async` and `await` for asynchronous execution.
8. **Error Handling**: The code includes try-except blocks to handle potential exceptions during subprocess execution and command processing.
9. **Output to User**: The code prints out the results of command execution, errors, and environment changes to the user.
Based on this review, the code in `plugins/gorilla_plugin.py` seems to meet the initially stated requirements and should behave as intended when executed. However, it's important to note that without running the code in its actual environment and without unit tests, we cannot guarantee that there are no logical errors or runtime issues.
If you have any specific concerns or additional requirements, please let me know, and we can review the code further.
USER: I added these *read-write* files: /home/anon/ubuntu-repos/EasyAGI/OAI_CONFIG_LIST.json
ASSISTANT: Great, now that the file `/home/anon/ubuntu-repos/EasyAGI/OAI_CONFIG_LIST.json` is marked as *read-write*, please let me know what changes or verifications you would like to perform on it. If there are any specific updates or checks you need, I'm here to assist you with that.
USER: actually, hold on a sec -- we install the gorilla-cli package as part of requirements.txt, can't we just import gorilla-cli at the top of the file?
ASSISTANT: Yes, if the `gorilla-cli` package provides a Python API that you can import and use within your code, you can certainly import it at the top of your `plugins/gorilla_plugin.py` file. This would allow you to use the functionality provided by `gorilla-cli` directly in your Python code, rather than invoking it as a subprocess.
However, before making this change, you should check the documentation for `gorilla-cli` to ensure that it does indeed offer a Python API and to understand how to use it properly.
If you confirm that `gorilla-cli` has a Python API and you want
^C KeyboardInterrupt
USER: i don't think gorilla-cli has a python api, as far as I know it's just a cli tool. if i've included it in requirements.txt, do i need to further instruct the user in the `README.md` to make some modifications accordingly, or is the code we've written so far sufficient for things to 'just work', so to speak?
ASSISTANT: If `gorilla-cli` is a command-line tool without a Python API and you've included it in `requirements.txt`, it means that it will be installed when the user runs `pip install -r requirements.txt`. However, there are a few considerations to ensure that things will "just work":
1. **Executable Path**: The `gorilla-cli` tool must be in the system's PATH so that it can be invoked from the Python script. If it's installed as a Python package, it's likely that it will be placed in a directory that's already in the PATH. Otherwise, you might need to instruct users to add the installation directory to their PATH.
2. **Usage Instructions**: If `gorilla-cli` requires any initial setup or configuration, you should provide instructions in the `README.md` file. This could include setting up configuration files, authentication, or other prerequisites.
3. **Error Handling**: Your Python code should handle cases where `gorilla-cli` is not installed or not found in the PATH. This could involve checking for the existence of the executable before trying to use it and providing a clear error message if it's not found.
4. **Version Compatibility**: If your Python code depends on specific features or behavior of `gorilla-cli`, you should specify the compatible version(s) in `requirements.txt` or in the `README.md`.
5. **Platform Compatibility**: Ensure that `gorilla-cli` is compatible with the platforms your users are likely to use. If there are platform-specific instructions, include those in the `README.md`.
If all these considerations are addressed, then the code you've written should be sufficient for things to work without further modifications from the user. It's always a good practice to test the installation and execution process on a clean environment to confirm that everything works as expected before releasing it to users.
USER: alright, i think all of those should be fine. i can't imagine they'd update a cli tool to no longer operate via it's own executable, so hopefully we're clear on versions. with that, I'm thinking we want to move forward and start modifying our `.env.example` file to include any environment variables we need for our plugin to work, and then I think i'd just like to, as a best practices sort of thing, modify the `OAI_CONFIG_LIST.json` to grab the sensitive information it manages from the environment rather than asking the user to hardcode it. We'll want to update the readme accordingly as well.
ASSISTANT: To proceed with the modifications, we'll need to update the `.env.example` file to include the necessary environment variables for the `GorillaPlugin`. Additionally, we'll modify the `OAI_CONFIG_LIST.json` to reference these environment variables instead of hardcoded sensitive information. Finally, we'll update the `README.md` to provide instructions on setting up the environment variables.
Let's start with the `.env.example` file. We need to add the path to the `gorilla-cli` executable as an environment variable. We'll call this variable `GORILLA_CLI_PATH`.
Here is the *SEARCH/REPLACE* block for the `.env.example` file:
.env.example
<source>python
<<<<<<< SEARCH
=======
# Path to the gorilla-cli executable
GORILLA_CLI_PATH=/path/to/gorilla-cli
>>>>>>> REPLACE
</source>
Next, we'll modify the `OAI_CONFIG_LIST.json` to use environment variables for sensitive information like API keys. We'll assume that the environment variables are named `OPENAI_API_KEY` and `AZURE_OPENAI_API_KEY` for OpenAI and Azure OpenAI API keys, respectively.
OAI_CONFIG_LIST.json
<source>json
<<<<<<< SEARCH
{
"model": "gpt-4",
"api_key": "sk-...7HG"
},
{
"model": "gpt-4-1106-preview",
"api_key": "sk-...7HG"
},
{
"model": "gpt-3.5-turbo-16k",
"api_key": "sk-...7HG"
},
{
"model": "gpt-4",
"api_key": "<your Azure OpenAI API key here>",
"api_base": "<your Azure OpenAI API base here>",
"api_type": "azure",
"api_version": "2023-07-01-preview"
},
{
"model": "gpt-3.5-turbo",
"api_key": "<your Azure OpenAI API key here>",
"api_base": "<your Azure OpenAI API base here>",
"api_type": "azure",
"api_version": "2023-07-01-preview"
}
=======
{
"model": "gpt-4",
"api_key": "${OPENAI_API_KEY}"
},
{
"model": "gpt-4-1106-preview",
"api_key": "${OPENAI_API_KEY}"
},
{
"model": "gpt-3.5-turbo-16k",
"api_key": "${OPENAI_API_KEY}"
},
{
"model": "gpt-4",
"api_key": "${AZURE_OPENAI_API_KEY}",
"api_base": "${AZURE_OPENAI_API_BASE}",
"api_type": "azure",
"api_version": "2023-07-01-preview"
},
{
"model": "gpt-3.5-turbo",
"api_key": "${AZURE_OPENAI_API_KEY}",
"api_base": "${AZURE_OPENAI_API_BASE}",
"api_type": "azure",
"api_version": "2023-07-01-preview"
}
>>>>>>> REPLACE
</source>
Lastly, we'll update the `README.md` to instruct users on how to set up their environment variables based on the `.env.example` file.
README.md
<source>markdown
<<<<<<< SEARCH
enter your keys into the space provided, eg:
```json
{
"model": "gpt-4",
"api_key": "<your OpenAI API key here>"
},
{
"model": "gpt-4",
"api_key": "<your Azure OpenAI API key here>",
"api_base": "<your Azure OpenAI API base here>",
"api_type": "azure",
"api_version": "2023-07-01-preview"
}
```
with your keys or Azure OpenAI deployments
=======
Copy the `.env.example` file to a new file named `.env` and update it with your actual configuration:
```bash
cp .env.example .env
nano .env
```
Fill in the `GORILLA_CLI_PATH`, `OPENAI_API_KEY`, and `AZURE_OPENAI_API_KEY` with the appropriate values. For example:
```
GORILLA_CLI_PATH=/usr/local/bin/gorilla-cli
OPENAI_API_KEY=your-openai-api-key
AZURE_OPENAI_API_KEY=your-azure-openai-api-key
AZURE_OPENAI_API_BASE=your-azure-openai-api-base
```
Make sure to replace `/usr/local/bin/gorilla-cli` with the actual path to your `gorilla-cli` executable, and the placeholder keys with your actual API keys.
>>>>>>> REPLACE
</source>
These changes will help to keep sensitive information out of the codebase and make it easier for users to configure the application securely.
Merging so we don't lose track of things before more prs come in
Update README.md
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