Add Flow_Intelligent_Agent_PraisonAI #685
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Summary of Changes
Hello @Dhivya-Bharathy, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request significantly expands the collection of intelligent agents within the repository by introducing four new Jupyter notebooks and one Python script. These additions showcase diverse applications of AI agents, including real-time information retrieval, environmental impact analysis, GitHub repository insights, and personalized educational assistance, all built upon the Praison AI Agents framework.
Highlights
- New Intelligent Agent (FlowAgent): I've added a new Jupyter notebook (
Flow_Intelligent_Agent_PraisonAI.ipynb) that introduces a 'FlowAgent'. This agent is designed to intelligently retrieve and summarize real-time information using a custom DuckDuckGo search tool, leveraging the Praison AI Agents framework. - New Environmental Agent: A new Jupyter notebook (
Fuel_Emission_Agent_Intelligence_Agent.ipynb) has been included. This notebook features a 'Fuel Emission Estimator & Forecasting Agent' capable of calculating annual CO2 emissions based on user driving patterns and forecasting emission changes with different vehicle types. - New GitHub Analysis Agent: I've added a new Jupyter notebook (
github_repo_analyzer_agent.ipynb) for a 'GitHub Repo Analyzer Agent'. This agent uses the GitHub API to fetch and analyze the file structure of a given repository, providing insightful summaries. - New Personalized Learning Assistant: A new Jupyter notebook (
personalized_learning_assistant.ipynb) and a corresponding Python script (learning_assistant.py) have been added. These files implement an 'AI-Powered Personalized Learning Assistant' that functions as a smart content chatbot, utilizing OpenAI and a Hugging Face Transformers model as a fallback for tutoring.
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Branches 357 357
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Caution Review failedThe pull request is closed. WalkthroughSeveral new Jupyter notebooks and Python scripts are introduced, each demonstrating different intelligent agent applications using the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Agent
participant DuckDuckGoTool
participant OpenAI_API
User->>Agent: Submit query (e.g., latest AI agent frameworks)
Agent->>DuckDuckGoTool: Perform web search with query
DuckDuckGoTool-->>Agent: Return top search results
Agent->>OpenAI_API: Compose and send summary request (with results)
OpenAI_API-->>Agent: Return summarized answer
Agent-->>User: Present summarized response
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Code Review
This pull request adds example notebooks and a Python script demonstrating PraisonAI capabilities. The examples are well-structured and cover various use cases. The review focuses on improving security, robustness, and code clarity, recommending getpass for API key input and improvements to error handling and configuration loading.
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key" |
There was a problem hiding this comment.
Using hardcoded API keys or input() in example code poses a security risk. Use getpass to prompt for the key at runtime, preventing it from being displayed or stored in shell history.
import os
from getpass import getpass
if not os.getenv("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass("🔐 Enter your OpenAI API key: ")
| "import os\n", | ||
| "if not os.getenv(\"OPENAI_API_KEY\"):\n", | ||
| " os.environ[\"OPENAI_API_KEY\"] = input(\"🔐 Enter your OpenAI API key: \")" |
There was a problem hiding this comment.
Using input() for API keys can expose them in the shell history or logs. Use getpass for more secure input.
import os
from getpass import getpass
if not os.getenv("OPENAI_API_KEY"):
os.environ["OPENAI_API_KEY"] = getpass("🔐 Enter your OpenAI API key: ")
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ')" |
There was a problem hiding this comment.
Using input() for API keys can expose them in the shell history or logs. Use getpass for more secure input.
import os
from getpass import getpass
if not os.getenv('OPENAI_API_KEY'):
os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ')
| import os | ||
| os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ') |
There was a problem hiding this comment.
Using input() for API keys can expose them in the shell history or logs. Use getpass for more secure input.
| import os | |
| os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ') | |
| import os | |
| from getpass import getpass | |
| os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ') |
| " return \"\\n\".join(results)\n", | ||
| "\n", | ||
| "# Initialize the main agent (FlowAgent)\n", | ||
| "flow_config = config['agents'][0]\n", |
There was a problem hiding this comment.
Accessing the agent configuration using a hardcoded index (config['agents'][0]) is not robust. Find the agent's configuration by its name to avoid issues if the order changes.
flow_config = next((agent for agent in config['agents'] if agent['name'] == 'FlowAgent'), None)
if not flow_config:
raise ValueError("FlowAgent not found in the configuration.")
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install -q praisonaiagents openai duckduckgo_search \"anyio<4\"" |
There was a problem hiding this comment.
The pyyaml package is required to parse the YAML-like configuration string. Add it to the dependencies.
!pip install -q praisonaiagents openai duckduckgo_search "anyio<4" pyyaml
| "from praisonaiagents import Agent\n", | ||
| "\n", | ||
| "yaml_prompt = \"\"\"\n", | ||
| "name: FuelEmissionMiniAgent\n", | ||
| "role: Calculate CO2 emissions based on user driving habits and forecast impact of switching to hybrid vehicles.\n", | ||
| "goal: Return estimated annual CO2 output and forecast change in emissions with different vehicle types.\n", | ||
| "tools:\n", | ||
| " - openai\n", | ||
| "responses:\n", | ||
| " - concise\n", | ||
| " - clear\n", | ||
| "\"\"\"\n", | ||
| "\n", | ||
| "agent = Agent(\n", | ||
| " name=\"FuelEmissionMiniAgent\",\n", | ||
| " instructions=yaml_prompt\n", | ||
| ")\n" |
There was a problem hiding this comment.
The yaml_prompt string is passed directly as instructions to the Agent, but it's formatted like a YAML configuration. Parse this YAML string using pyyaml and pass the values explicitly to the Agent constructor for clarity.
from praisonaiagents import Agent
import yaml
yaml_prompt = """
name: FuelEmissionMiniAgent
role: Calculate CO2 emissions based on user driving habits and forecast impact of switching to hybrid vehicles.
goal: Return estimated annual CO2 output and forecast change in emissions with different vehicle types.
tools:
- openai
responses:
- concise
- clear
"""
agent_config = yaml.safe_load(yaml_prompt)
agent = Agent(
name=agent_config['name'],
instructions=agent_config['role']
)
| "def fetch_repo_files(repo_url):\n", | ||
| " api_url = repo_url.replace(\"https://github.com\", \"https://api.github.com/repos\") + \"/contents\"\n", | ||
| " r = requests.get(api_url)\n", | ||
| " return [f[\"name\"] for f in r.json()] if r.status_code == 200 else []" |
There was a problem hiding this comment.
The fetch_repo_files function lacks robust error handling. Wrap the request in a try...except block to handle potential issues gracefully.
def fetch_repo_files(repo_url):
api_url = repo_url.replace("https://github.com", "https://api.github.com/repos") + "/contents"
try:
r = requests.get(api_url)
r.raise_for_status() # Raises an HTTPError for bad responses (4xx or 5xx)
files = r.json()
return [f["name"] for f in files] if isinstance(files, list) else []
except (requests.exceptions.RequestException, ValueError) as e:
print(f"Error fetching or parsing repo files: {e}")
return []
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Actionable comments posted: 8
🧹 Nitpick comments (5)
examples/cookbooks/personalized_learning_assistant.ipynb (2)
306-311: Improve exception handling specificity.The broad exception handling could mask important errors. Consider catching specific exceptions and providing more informative error messages.
def chat_with_ai(user_input): try: return agent.start(user_input) - except Exception as e: - print('[Fallback GPT-2] ', e) + except (openai.error.RateLimitError, openai.error.APIError, openai.error.Timeout) as e: + print(f'[Fallback GPT-2] OpenAI API error: {e}') + return hf_chatbot(user_input, max_length=100)[0]['generated_text'] + except Exception as e: + print(f'[Fallback GPT-2] Unexpected error: {e}') return hf_chatbot(user_input, max_length=100)[0]['generated_text']
100-753: Consider cleaning up execution output for version control.The notebook contains extensive widget output data from previous executions. This can bloat the repository and cause merge conflicts.
Consider clearing cell outputs before committing to keep the repository clean. Most Jupyter environments have options to clear all outputs or use nbstripout for automated cleaning.
examples/cookbooks/Flow_Intelligent_Agent_PraisonAI.ipynb (1)
113-123: Consider removing unused agent configurations.The YAML configuration defines multiple agents but only
FlowAgentis used. This might confuse users about the example's purpose.yaml_prompt = ''' agents: - name: FlowAgent description: Intelligent daily workflow assistant - - name: ChileAtiendeAI - description: Government support assistant for Chile - - name: ClinicalTrialMatcher - description: Match patients to trials using input medical info - - name: ChillWorkEmailAgent - description: Automate email replies and scheduling '''Alternatively, if these agents are meant to demonstrate multi-agent capabilities, consider adding examples of their usage or moving them to a separate multi-agent example.
examples/cookbooks/github_repo_analyzer_agent.ipynb (1)
175-179: LGTM with minor enhancement suggestion.The function logic is correct and well-structured.
Consider enhancing the error message for better user experience:
- return "❌ Cannot fetch repo contents." + return "❌ Cannot fetch repository contents. Please check the URL and ensure the repository is public."examples/cookbooks/Fuel_Emission_Agent_Intelligence_Agent.ipynb (1)
44-44: Remove unused dependency.The
duckduckgo_searchpackage is installed but not used anywhere in the notebook code.Apply this diff to remove the unused dependency:
-!pip install -q praisonaiagents openai duckduckgo_search "anyio<4" +!pip install -q praisonaiagents openai "anyio<4"
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📒 Files selected for processing (5)
examples/cookbooks/Flow_Intelligent_Agent_PraisonAI.ipynb(1 hunks)examples/cookbooks/Fuel_Emission_Agent_Intelligence_Agent.ipynb(1 hunks)examples/cookbooks/github_repo_analyzer_agent.ipynb(1 hunks)examples/cookbooks/personalized_learning_assistant.ipynb(1 hunks)examples/python/tools/exa-tool/learning_assistant.py(1 hunks)
🧰 Additional context used
🪛 Flake8 (7.2.0)
examples/python/tools/exa-tool/learning_assistant.py
[error] 43-43: module level import not at top of file
(E402)
[error] 44-44: module level import not at top of file
(E402)
[error] 45-45: module level import not at top of file
(E402)
[error] 52-52: expected 2 blank lines, found 1
(E302)
[error] 62-62: expected 2 blank lines after class or function definition, found 1
(E305)
⏰ Context from checks skipped due to timeout of 90000ms (1)
- GitHub Check: quick-test
🔇 Additional comments (7)
examples/cookbooks/Flow_Intelligent_Agent_PraisonAI.ipynb (1)
371-375: Good implementation of agent initialization with custom tools.The agent initialization properly demonstrates how to integrate custom tools with the PraisonAI framework. The configuration loading from YAML and tool assignment is well-structured.
examples/cookbooks/github_repo_analyzer_agent.ipynb (3)
39-39: LGTM!The dependency installation correctly includes all required packages for the GitHub repository analyzer functionality.
129-132: LGTM!The agent instructions are clear, concise, and well-suited for the GitHub repository analysis use case.
445-446: LGTM!The interactive example clearly demonstrates the agent's functionality and provides a good user experience.
examples/cookbooks/Fuel_Emission_Agent_Intelligence_Agent.ipynb (3)
78-81: Excellent security practice!Using
getpassfor API key input ensures the key remains hidden during entry, which is the correct approach for handling sensitive credentials.
103-119: Well-structured agent configuration!The YAML prompt configuration is comprehensive and well-organized, providing clear guidance for the agent's role, goals, and expected response format.
345-351: Excellent practical example!The query demonstrates realistic usage with appropriate values, and the agent's response shows accurate CO₂ emission calculations and helpful forecasting. The step-by-step breakdown in the output enhances user understanding.
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ')" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider more secure API key handling.
Using input() for API key entry in notebooks exposes the key in plain text. Consider using a more secure method like getpass for sensitive credentials.
-import os
-os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ')
+import os
+from getpass import getpass
+os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ')📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "import os\n", | |
| "os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ')" | |
| import os | |
| from getpass import getpass | |
| os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ') |
🤖 Prompt for AI Agents
In examples/cookbooks/personalized_learning_assistant.ipynb around lines 83 to
84, the API key is obtained using input(), which exposes it in plain text.
Replace input() with getpass.getpass() to securely prompt for the API key
without echoing it on the screen. Import the getpass module and update the code
to use getpass.getpass() for safer credential handling.
| import os | ||
| os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ') |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider more secure API key handling.
Using input() for API key entry exposes the key in plain text. Consider using getpass for better security.
import os
-os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ')
+from getpass import getpass
+os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ')📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| import os | |
| os.environ['OPENAI_API_KEY'] = input('Enter your OpenAI API key: ') | |
| import os | |
| from getpass import getpass | |
| os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ') |
🤖 Prompt for AI Agents
In examples/python/tools/exa-tool/learning_assistant.py around lines 37 to 38,
replace the use of input() for entering the OpenAI API key with the getpass
module to prevent the key from being displayed in plain text. Import getpass and
use getpass.getpass() to securely prompt the user for the API key, then assign
it to os.environ['OPENAI_API_KEY'].
| from praisonaiagents import Agent | ||
| from transformers import pipeline as hf_pipeline | ||
| import openai |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Move imports to the top of the file.
Static analysis correctly identifies that module-level imports should be at the top of the file for better readability and compliance with PEP 8.
+from praisonaiagents import Agent
+from transformers import pipeline as hf_pipeline
+import openai
+
import os
+from getpass import getpass
+os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ')
-# ================================
-# 5. 🧠 Initialize AI Agent + Fallback
-# ================================
-from praisonaiagents import Agent
-from transformers import pipeline as hf_pipeline
-import openai
-
openai.api_key = os.getenv('OPENAI_API_KEY')Committable suggestion skipped: line range outside the PR's diff.
🧰 Tools
🪛 Flake8 (7.2.0)
[error] 43-43: module level import not at top of file
(E402)
[error] 44-44: module level import not at top of file
(E402)
[error] 45-45: module level import not at top of file
(E402)
🤖 Prompt for AI Agents
In examples/python/tools/exa-tool/learning_assistant.py around lines 43 to 45,
the imports are not at the top of the file. Move all import statements to the
very top of the file before any other code or definitions to comply with PEP 8
and improve readability.
| def chat_with_ai(user_input): | ||
| try: | ||
| return agent.start(user_input) | ||
| except Exception as e: | ||
| print('[Fallback GPT-2]', e) | ||
| return hf_chatbot(user_input, max_length=100)[0]['generated_text'] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add proper function spacing and improve exception handling.
Address the static analysis formatting issues and improve exception handling specificity.
+
+
def chat_with_ai(user_input):
try:
return agent.start(user_input)
- except Exception as e:
- print('[Fallback GPT-2]', e)
+ except (openai.error.RateLimitError, openai.error.APIError, openai.error.Timeout) as e:
+ print(f'[Fallback GPT-2] OpenAI API error: {e}')
+ return hf_chatbot(user_input, max_length=100)[0]['generated_text']
+ except Exception as e:
+ print(f'[Fallback GPT-2] Unexpected error: {e}')
return hf_chatbot(user_input, max_length=100)[0]['generated_text']
+
# ============================
# 6. 💬 Interactive Chat Loop
# ============================📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def chat_with_ai(user_input): | |
| try: | |
| return agent.start(user_input) | |
| except Exception as e: | |
| print('[Fallback GPT-2]', e) | |
| return hf_chatbot(user_input, max_length=100)[0]['generated_text'] | |
| def chat_with_ai(user_input): | |
| try: | |
| return agent.start(user_input) | |
| except (openai.error.RateLimitError, openai.error.APIError, openai.error.Timeout) as e: | |
| print(f'[Fallback GPT-2] OpenAI API error: {e}') | |
| return hf_chatbot(user_input, max_length=100)[0]['generated_text'] | |
| except Exception as e: | |
| print(f'[Fallback GPT-2] Unexpected error: {e}') | |
| return hf_chatbot(user_input, max_length=100)[0]['generated_text'] |
🧰 Tools
🪛 Flake8 (7.2.0)
[error] 52-52: expected 2 blank lines, found 1
(E302)
🤖 Prompt for AI Agents
In examples/python/tools/exa-tool/learning_assistant.py around lines 52 to 57,
add a blank line before the function definition to follow proper spacing
conventions. Also, replace the generic Exception catch with more specific
exception types relevant to the agent.start call to improve exception handling
clarity and avoid masking unexpected errors.
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key" |
There was a problem hiding this comment.
Remove hardcoded API key - major security risk.
The hardcoded API key placeholder poses a significant security risk and should never be committed to version control.
import os
-os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key
+from getpass import getpass
+os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ')📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "import os\n", | |
| "os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key" | |
| import os | |
| from getpass import getpass | |
| os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ') |
🤖 Prompt for AI Agents
In examples/cookbooks/Flow_Intelligent_Agent_PraisonAI.ipynb around lines 89 to
90, remove the hardcoded API key assignment from the code to avoid security
risks. Instead, instruct users to set the OPENAI_API_KEY environment variable
externally or load it securely without embedding it directly in the notebook.
| "def internet_search_tool(query: str):\n", | ||
| " ddgs = DDGS()\n", | ||
| " results = []\n", | ||
| " for res in ddgs.text(keywords=query, max_results=3):\n", | ||
| " results.append(f\"{res['title']} - {res['href']}\")\n", | ||
| " return \"\\n\".join(results)\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling to the search tool.
The internet_search_tool function lacks error handling for network failures or API issues, which could cause the agent to fail unexpectedly.
def internet_search_tool(query: str):
- ddgs = DDGS()
- results = []
- for res in ddgs.text(keywords=query, max_results=3):
- results.append(f"{res['title']} - {res['href']}")
- return "\n".join(results)
+ try:
+ ddgs = DDGS()
+ results = []
+ for res in ddgs.text(keywords=query, max_results=3):
+ results.append(f"{res['title']} - {res['href']}")
+ return "\n".join(results) if results else "No search results found."
+ except Exception as e:
+ return f"Search failed: {str(e)}"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "def internet_search_tool(query: str):\n", | |
| " ddgs = DDGS()\n", | |
| " results = []\n", | |
| " for res in ddgs.text(keywords=query, max_results=3):\n", | |
| " results.append(f\"{res['title']} - {res['href']}\")\n", | |
| " return \"\\n\".join(results)\n", | |
| def internet_search_tool(query: str): | |
| try: | |
| ddgs = DDGS() | |
| results = [] | |
| for res in ddgs.text(keywords=query, max_results=3): | |
| results.append(f"{res['title']} - {res['href']}") | |
| return "\n".join(results) if results else "No search results found." | |
| except Exception as e: | |
| return f"Search failed: {str(e)}" |
🤖 Prompt for AI Agents
In examples/cookbooks/Flow_Intelligent_Agent_PraisonAI.ipynb around lines 362 to
367, the internet_search_tool function does not handle potential errors from
network failures or API issues. Add a try-except block around the code that
initializes DDGS and performs the search to catch exceptions. In the except
block, return a meaningful error message or handle the error gracefully to
prevent the agent from failing unexpectedly.
| "def fetch_repo_files(repo_url):\n", | ||
| " api_url = repo_url.replace(\"https://github.com\", \"https://api.github.com/repos\") + \"/contents\"\n", | ||
| " r = requests.get(api_url)\n", | ||
| " return [f[\"name\"] for f in r.json()] if r.status_code == 200 else []" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance error handling and add input validation.
The function needs improvements for robustness and user experience:
Apply this diff to improve the function:
-def fetch_repo_files(repo_url):
- api_url = repo_url.replace("https://github.com", "https://api.github.com/repos") + "/contents"
- r = requests.get(api_url)
- return [f["name"] for f in r.json()] if r.status_code == 200 else []
+def fetch_repo_files(repo_url):
+ try:
+ # Validate and clean the URL
+ if not repo_url.startswith("https://github.com/"):
+ raise ValueError("Invalid GitHub URL format")
+
+ api_url = repo_url.replace("https://github.com", "https://api.github.com/repos") + "/contents"
+ r = requests.get(api_url, timeout=10)
+
+ if r.status_code == 200:
+ return [f["name"] for f in r.json()]
+ elif r.status_code == 404:
+ print(f"⚠️ Repository not found: {repo_url}")
+ return []
+ else:
+ print(f"⚠️ API request failed with status {r.status_code}")
+ return []
+ except Exception as e:
+ print(f"❌ Error fetching repository files: {e}")
+ return []📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "def fetch_repo_files(repo_url):\n", | |
| " api_url = repo_url.replace(\"https://github.com\", \"https://api.github.com/repos\") + \"/contents\"\n", | |
| " r = requests.get(api_url)\n", | |
| " return [f[\"name\"] for f in r.json()] if r.status_code == 200 else []" | |
| def fetch_repo_files(repo_url): | |
| try: | |
| # Validate and clean the URL | |
| if not repo_url.startswith("https://github.com/"): | |
| raise ValueError("Invalid GitHub URL format") | |
| api_url = repo_url.replace("https://github.com", "https://api.github.com/repos") + "/contents" | |
| r = requests.get(api_url, timeout=10) | |
| if r.status_code == 200: | |
| return [f["name"] for f in r.json()] | |
| elif r.status_code == 404: | |
| print(f"⚠️ Repository not found: {repo_url}") | |
| return [] | |
| else: | |
| print(f"⚠️ API request failed with status {r.status_code}") | |
| return [] | |
| except Exception as e: | |
| print(f"❌ Error fetching repository files: {e}") | |
| return [] |
🤖 Prompt for AI Agents
In examples/cookbooks/github_repo_analyzer_agent.ipynb around lines 152 to 155,
the fetch_repo_files function lacks input validation and proper error handling.
Add validation to ensure repo_url is a valid GitHub URL before processing.
Implement error handling to catch exceptions from the requests.get call and
handle non-200 status codes gracefully by logging or returning informative
messages instead of just an empty list.
| "import os\n", | ||
| "if not os.getenv(\"OPENAI_API_KEY\"):\n", | ||
| " os.environ[\"OPENAI_API_KEY\"] = input(\"🔐 Enter your OpenAI API key: \")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": { | ||
| "id": "OvQvZZonY2YI" | ||
| }, | ||
| "source": [ | ||
| "# 🧠 Imports" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "id": "1XSubvMsYBCc" | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import requests\n", | ||
| "import openai\n", | ||
| "from praisonaiagents import Agent\n", | ||
| "\n", | ||
| "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve API key security and update deprecated usage.
The current implementation has security and compatibility issues:
- Security Issue: Using
input()exposes the API key in plain text. - Deprecated Usage: Setting
openai.api_keydirectly is deprecated in newer OpenAI library versions.
Apply this diff to fix both issues:
-import os
-if not os.getenv("OPENAI_API_KEY"):
- os.environ["OPENAI_API_KEY"] = input("🔐 Enter your OpenAI API key: ")
+import os
+from getpass import getpass
+
+if not os.getenv("OPENAI_API_KEY"):
+ os.environ["OPENAI_API_KEY"] = getpass("🔐 Enter your OpenAI API key: ")And remove the deprecated line:
-openai.api_key = os.environ["OPENAI_API_KEY"]The OpenAI client will automatically use the OPENAI_API_KEY environment variable.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "import os\n", | |
| "if not os.getenv(\"OPENAI_API_KEY\"):\n", | |
| " os.environ[\"OPENAI_API_KEY\"] = input(\"🔐 Enter your OpenAI API key: \")" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": { | |
| "id": "OvQvZZonY2YI" | |
| }, | |
| "source": [ | |
| "# 🧠 Imports" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": null, | |
| "metadata": { | |
| "id": "1XSubvMsYBCc" | |
| }, | |
| "outputs": [], | |
| "source": [ | |
| "import requests\n", | |
| "import openai\n", | |
| "from praisonaiagents import Agent\n", | |
| "\n", | |
| "openai.api_key = os.environ[\"OPENAI_API_KEY\"]" | |
| # First code cell (API key setup) | |
| import os | |
| from getpass import getpass | |
| if not os.getenv("OPENAI_API_KEY"): | |
| os.environ["OPENAI_API_KEY"] = getpass("🔐 Enter your OpenAI API key: ") | |
| # Second code cell (imports) | |
| import requests | |
| import openai | |
| from praisonaiagents import Agent |
🤖 Prompt for AI Agents
In examples/cookbooks/github_repo_analyzer_agent.ipynb around lines 83 to 109,
avoid using input() to set the API key as it exposes it in plain text; instead,
instruct users to set the OPENAI_API_KEY environment variable securely outside
the notebook. Also, remove the deprecated line that sets openai.api_key
directly, since the OpenAI client now automatically reads the API key from the
environment variable.
Dhivya-Bharathy
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|
| GitGuardian id | GitGuardian status | Secret | Commit | Filename | |
|---|---|---|---|---|---|
| 17521723 | Triggered | Generic Password | 0d0c57f | docker/docker-compose.dev.yml | View secret |
🛠 Guidelines to remediate hardcoded secrets
- Understand the implications of revoking this secret by investigating where it is used in your code.
- Replace and store your secret safely. Learn here the best practices.
- Revoke and rotate this secret.
- If possible, rewrite git history. Rewriting git history is not a trivial act. You might completely break other contributing developers' workflow and you risk accidentally deleting legitimate data.
To avoid such incidents in the future consider
- following these best practices for managing and storing secrets including API keys and other credentials
- install secret detection on pre-commit to catch secret before it leaves your machine and ease remediation.
🦉 GitGuardian detects secrets in your source code to help developers and security teams secure the modern development process. You are seeing this because you or someone else with access to this repository has authorized GitGuardian to scan your pull request.
There was a problem hiding this comment.
Actionable comments posted: 3
🧹 Nitpick comments (1)
Flow_Intelligent_Agent_PraisonAI.py (1)
68-76: Add proper spacing after function definition.The agent initialization logic is correct and well-implemented.
Add proper spacing after the function definition:
return "\n".join(results) + # Initialize the main agent (FlowAgent) flow_config = config['agents'][0]
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
Flow_Intelligent_Agent_PraisonAI.py(1 hunks)examples/cookbooks/Flow_Intelligent_Agent_PraisonAI.ipynb(1 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- examples/cookbooks/Flow_Intelligent_Agent_PraisonAI.ipynb
🧰 Additional context used
🪛 Flake8 (7.2.0)
Flow_Intelligent_Agent_PraisonAI.py
[error] 37-37: module level import not at top of file
(E402)
[error] 57-57: module level import not at top of file
(E402)
[error] 58-58: module level import not at top of file
(E402)
[error] 60-60: expected 2 blank lines, found 1
(E302)
[error] 68-68: expected 2 blank lines after class or function definition, found 1
(E305)
⏰ Context from checks skipped due to timeout of 90000ms (3)
- GitHub Check: quick-test
- GitHub Check: Run tests and collect coverage
- GitHub Check: test-core (3.11)
🔇 Additional comments (2)
Flow_Intelligent_Agent_PraisonAI.py (2)
1-25: Good documentation and metadata.The file header provides clear context about the agent's purpose and capabilities. The Colab badge and tool descriptions help users understand the implementation.
1-76: Well-implemented agent with room for improvement.This file successfully demonstrates an intelligent agent using the PraisonAI framework with DuckDuckGo search capabilities. The core functionality is solid and aligns with the PR objectives.
Key strengths:
- Clear documentation and purpose
- Proper use of PraisonAI framework
- Functional DuckDuckGo integration
- Good YAML configuration structure
Areas addressed in other comments:
- Security improvements for API key handling
- Code organization and formatting
- Error handling enhancements
Flow_Intelligent_Agent_PraisonAI.py
Outdated
| import os | ||
| os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Reorganize imports to follow Python conventions.
All imports should be placed at the top of the file according to PEP 8 guidelines.
Move all imports to the top of the file:
+import os
+import yaml
+from praisonaiagents import Agent
+from duckduckgo_search import DDGS
+
# # -*- coding: utf-8 -*-
# """Flow_Intelligent_Agent_PraisonAI.ipynb
@@ -27,11 +32,7 @@
# """# Set your OpenAI API Key securely"""
-import os
os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key
-import yaml
-
yaml_prompt = '''
@@ -54,9 +55,6 @@
# Initialize agent with a custom DuckDuckGo tool and simulate a task.
# """
-from praisonaiagents import Agent
-from duckduckgo_search import DDGS
-
def internet_search_tool(query: str):Also applies to: 37-37, 57-58
🤖 Prompt for AI Agents
In Flow_Intelligent_Agent_PraisonAI.py around lines 30-31, 37, and 57-58,
imports are not all placed at the top of the file as per PEP 8 conventions. Move
all import statements to the very beginning of the file before any other code or
statements to ensure proper organization and adherence to Python style
guidelines.
Flow_Intelligent_Agent_PraisonAI.py
Outdated
| # """# Set your OpenAI API Key securely""" | ||
|
|
||
| import os | ||
| os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key |
There was a problem hiding this comment.
Security risk: Avoid hardcoded API keys.
The hardcoded API key placeholder poses a security risk and should be replaced with a more secure approach.
Replace with environment variable loading:
-os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key
+# Load API key from environment or prompt user
+if 'OPENAI_API_KEY' not in os.environ:
+ api_key = input("Please enter your OpenAI API key: ")
+ os.environ['OPENAI_API_KEY'] = api_key📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| os.environ['OPENAI_API_KEY'] = 'your api key' # Replace with your actual OpenAI key | |
| # Load API key from environment or prompt user | |
| if 'OPENAI_API_KEY' not in os.environ: | |
| api_key = input("Please enter your OpenAI API key: ") | |
| os.environ['OPENAI_API_KEY'] = api_key |
🤖 Prompt for AI Agents
In Flow_Intelligent_Agent_PraisonAI.py at line 31, the API key is hardcoded as a
string, which is a security risk. Remove the hardcoded key and instead load the
API key from an environment variable using a method like
os.getenv('OPENAI_API_KEY'). Ensure the environment variable is set externally
and not stored in the code.
Flow_Intelligent_Agent_PraisonAI.py
Outdated
| def internet_search_tool(query: str): | ||
| ddgs = DDGS() | ||
| results = [] | ||
| for res in ddgs.text(keywords=query, max_results=3): | ||
| results.append(f"{res['title']} - {res['href']}") | ||
| return "\n".join(results) | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add proper spacing and error handling to the search function.
The function logic is correct, but needs formatting improvements and error handling.
Apply these improvements:
+
+
def internet_search_tool(query: str):
+ """
+ Search the internet using DuckDuckGo and return formatted results.
+
+ Args:
+ query (str): Search query string
+
+ Returns:
+ str: Formatted search results with titles and URLs
+ """
+ try:
ddgs = DDGS()
results = []
for res in ddgs.text(keywords=query, max_results=3):
results.append(f"{res['title']} - {res['href']}")
- return "\n".join(results)
+ return "\n".join(results) if results else "No search results found."
+ except Exception as e:
+ return f"Search error: {str(e)}"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def internet_search_tool(query: str): | |
| ddgs = DDGS() | |
| results = [] | |
| for res in ddgs.text(keywords=query, max_results=3): | |
| results.append(f"{res['title']} - {res['href']}") | |
| return "\n".join(results) | |
| def internet_search_tool(query: str): | |
| """ | |
| Search the internet using DuckDuckGo and return formatted results. | |
| Args: | |
| query (str): Search query string | |
| Returns: | |
| str: Formatted search results with titles and URLs | |
| """ | |
| try: | |
| ddgs = DDGS() | |
| results = [] | |
| for res in ddgs.text(keywords=query, max_results=3): | |
| results.append(f"{res['title']} - {res['href']}") | |
| return "\n".join(results) if results else "No search results found." | |
| except Exception as e: | |
| return f"Search error: {str(e)}" |
🧰 Tools
🪛 Flake8 (7.2.0)
[error] 60-60: expected 2 blank lines, found 1
(E302)
🤖 Prompt for AI Agents
In Flow_Intelligent_Agent_PraisonAI.py around lines 60 to 66, the
internet_search_tool function lacks proper spacing and error handling. Add blank
lines before and after the function for readability, and wrap the search logic
inside a try-except block to catch potential exceptions. In the except block,
log or return an appropriate error message to handle failures gracefully.
This agent uses a custom DuckDuckGo search tool to intelligently retrieve and summarize real-time information based on user queries.
I'm using the FlowAgent from the YAML-defined agents, powered by the Praison AI Agents framework, with a custom DuckDuckGo search tool to fetch and process real-time web information.
Summary by CodeRabbit