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3, because the PR includes a significant amount of changes across multiple files, involving both backend and frontend components. The changes include updates to AI models, introduction of new system prompts, and adjustments to logging and temperature settings, which require careful review to ensure they integrate well without introducing bugs or performance issues.
🧪 Relevant tests
No
🔍 Possible issues
Possible Bug: The method set_files_in_prompt in SystemPromptHandler might not handle the anth parameter correctly if it's not explicitly set to True or False, as the method's behavior significantly changes based on this flag.
Performance Concern: The new system prompt DEFAULT_SYSTEM_PROMPT_V2 includes a lot of static text and structured commands which might not be optimized for frequent updates or modifications, potentially leading to maintenance challenges.
🔒 Security concerns
No
Code feedback:
relevant file
backend/agent/coding_agent.py
suggestion
Consider adding a check for self.GPT_MODEL in the call_model_streaming method to ensure it's not None before proceeding with operations. This can prevent potential runtime errors if the model is not set. [important]
For the new DEFAULT_SYSTEM_PROMPT_V2, consider breaking down the static content into smaller, reusable components or templates that can be dynamically assembled. This approach can improve maintainability and flexibility of prompt modifications. [important]
Refactor the StreamToLogger class to handle potential recursive calls to write more gracefully by using a reentrant lock instead of a boolean flag. This can prevent issues where logging inside a logging call might skip certain log messages. [medium]
Implement error handling in the UI, such as displaying a notification to the user when the temperature update fails. This improves user experience by providing feedback on the success or failure of their actions. [medium]
Overview:
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Refactor the long string into smaller functions to improve code maintainability.
The DEFAULT_SYSTEM_PROMPT_V2 string is very long and contains multiple responsibilities and information. It would be beneficial to split this into smaller, more manageable components or functions, each handling a specific aspect of the prompt setup. This would improve readability and maintainability.
Add explicit handling for when self.GPT_MODEL is None to avoid unintended behavior.
The call_model_streaming method has a condition that checks if self.GPT_MODEL starts with "gpt" or is None, which could lead to unexpected behavior if self.GPT_MODEL is None because the subsequent code assumes a valid model name. It's better to separate these conditions and handle the None case explicitly.
-if self.GPT_MODEL.startswith("gpt") or self.GPT_MODEL is None:+if self.GPT_MODEL is None:+ raise ValueError("GPT_MODEL is not set")+elif self.GPT_MODEL.startswith("gpt"):
print("Calling OpenAI")
for chunk in self.client.chat.completions.create(**kwargs):
yield chunk
Enhancement
Simplify the logging mechanism in StreamToLogger to avoid unnecessary state management.
The StreamToLogger class method write uses a flag _is_logging to prevent recursive logging. However, this approach can be simplified by directly checking if the message is not empty, which avoids the need for an additional flag and makes the code cleaner.
def write(self, message):
- if not self._is_logging:- self._is_logging = True+ if message.strip():
self.logger.log(self.log_level, message.rstrip())
- self._is_logging = False
Add error handling to the anthropic model branch to manage exceptions gracefully.
The call_model_streaming method has a branch for handling an "anthropic" model, but it lacks a clear exception or error handling if the generate_anthropic_prompt method fails. It would be beneficial to add error handling to manage exceptions more gracefully.
Improve error handling in the database connection function to manage connection failures.
The create_database_connection function attempts to connect to a SQLite database but does not handle potential exceptions that could arise during connection, such as sqlite3.OperationalError. Adding a try-except block would make the function more robust by handling these exceptions.
Improve the robustness of string concatenation in system prompt construction.
Replace the direct string concatenation with a more robust and error-free method using str.join(). This will ensure that the system prompt is constructed correctly even if some parts are missing or None.
-self.system = (- self.identity- + "\n\n"- + "The following information is intended to aid in your responses to the User\n\n"- + "The project directory is setup as follows:\n"-)-self.system += self.tree + "\n\n" if self.tree else ""+parts = [+ self.identity,+ "\n\nThe following information is intended to aid in your responses to the User\n\n",+ "The project directory is setup as follows:\n",+ self.tree + "\n\n" if self.tree else ""+]+self.system = "".join(filter(None, parts))
Replace subprocess with GitPython for executing git commands.
Avoid using subprocess to run git commands directly. Instead, use a library like GitPython for better error handling and integration.
tokens = self.memory_manager.get_total_tokens_in_message(message)
+self.assertEqual(tokens, expected_tokens_count) # expected_tokens_count needs to be defined based on the expected outcome
Best practice
Replace print with logging.info for consistent logging practices.
Replace the use of print for logging with the appropriate logging level method to maintain consistency and control over logging output.
AGENT.memory_manager.add_message(
"assistant",
accumulated_messages[id],
system_prompt=AGENT.memory_manager.prompt_handler.system,
)
+# Ensure that the `system_prompt` is being used inside the `add_message` method
Add a check to ensure the list has enough elements before accessing by index.
To avoid potential issues with index out of range, add a check to ensure messages list has the expected number of elements before accessing by index.
temperature = input.get("temperature")
if temperature is not None:
- AGENT.temperature = temperature- logging.info(f"Setting system temperature to: {temperature}")- return JSONResponse(- status_code=200, content={"message": "Temperature set successfully"}- )+ try:+ # Validate and possibly convert temperature to a safe format+ safe_temperature = float(temperature) # Example of type casting for validation+ AGENT.temperature = safe_temperature+ logging.info(f"Setting system temperature to: {safe_temperature}")+ return JSONResponse(+ status_code=200, content={"message": "Temperature set successfully"}+ )+ except ValueError:+ return JSONResponse(+ status_code=400, content={"error": "Invalid temperature value"}+ )
Enhance security by using parameterized SQL queries.
Use parameterized queries to prevent SQL injection vulnerabilities. Replace direct string insertion into SQL queries with placeholders and parameter passing.
-self.cur.execute("DELETE FROM system_prompt")+self.cur.execute("DELETE FROM system_prompt WHERE role = ?", ("system",))
self.cur.execute(
"INSERT INTO system_prompt (role, content) VALUES (?, ?)",
("system", self.system),
)
Performance
Optimize JSON serialization for better performance in streaming contexts.
Replace the use of json.dumps with a more efficient method or library that can handle streaming JSON data more effectively, especially in high-load environments.
-self.cur.execute("SELECT * FROM system_prompts")+self.cur.execute("SELECT id, prompt, created_at, updated_at FROM system_prompts")
return [
- {- "id": prompt[0],- "name": prompt[0],- "prompt": prompt[1],- "created_at": prompt[2],- "updated_at": prompt[3],- }- for prompt in self.cur.fetchall()+ {"id": id, "name": id, "prompt": prompt, "created_at": created_at, "updated_at": updated_at}+ for id, prompt, created_at, updated_at in self.cur.fetchall()
]
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User description
/describe
Type
enhancement, bug_fix
Description
DEFAULT_SYSTEM_PROMPT_V2for enhanced logical reasoning tasks.gpt-4-turboand adjusted the temperature setting for more deterministic outputs./set_temperaturefor dynamic temperature adjustments.MemoryManagerandSystemPromptHandlerfor improved efficiency and functionality.Changes walkthrough
7 files
agent_prompts.py
Introduce New System Prompt and Refine Existing Promptbackend/agent/agent_prompts.py
DEFAULT_SYSTEM_PROMPT_V2with detailedinstructions and examples for logical reasoning tasks.
DEFAULT_SYSTEM_PROMPTto include a morestructured and detailed guide for code integration tasks.
coding_agent.py
Update AI Model and Refine Streaming Methodbackend/agent/coding_agent.py
gpt-4-0125-previewtogpt-4-turbo.deterministic outputs.
call_model_streamingmethod by removing unnecessaryprint statements and handling different model types more cleanly.
main.py
Add Temperature Setting Endpoint and Enhance Loggingbackend/main.py
/set_temperatureto adjust system temperaturesettings.
memory_manager.py
Refactor MemoryManager for Improved Efficiencybackend/memory/memory_manager.py
MemoryManagerclass, removing unused methods andstreamlining the message handling.
system_prompt_handler.py
Refactor SystemPromptHandler for Better Prompt Managementbackend/memory/system_prompt_handler.py
SystemPromptHandlerto manage system prompts moreeffectively, including CRUD operations and diff generation from the
main branch.
working_context.py
Implement WorkingContext for User Profile Managementbackend/memory/working_context.py
WorkingContextto manage user profiles andrelations using RDF and SPARQL.
DirectorySelectOption.js
Add Frontend Support for Temperature Adjustmentfrontend/components/DirectorySelectOption.js
endpoint.
1 files
app_setup.py
Integrate New System Prompt and Adjust Logging Levelbackend/app_setup.py
DEFAULT_SYSTEM_PROMPT_V2into the app setup.1 files
my_codebase.py
Clean-up Database Codebase Access Methodbackend/database/my_codebase.py
treemethod, removing unnecessary printstatements.