Add PraisonAI QA Automation Copilot Notebook #703
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WalkthroughTwo new Jupyter notebooks have been added. The first demonstrates a prototype cybersecurity agent for validating PoC exploits against CVE attack intents. The second showcases an AI chatbot assistant that answers questions about Chilean government services, integrating translation and Firecrawl API-based search. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant AttackIntentAgent
participant ValidationAgent
User->>+run_pocky_for_cve: Provide CVE ID
run_pocky_for_cve->>AttackIntentAgent: Extract attack intent from CVE description
AttackIntentAgent-->>run_pocky_for_cve: Return attack intent
run_pocky_for_cve->>ValidationAgent: Validate PoC against attack intent
ValidationAgent-->>run_pocky_for_cve: Return validation result
run_pocky_for_cve-->>User: Display result
sequenceDiagram
participant User
participant Translator
participant FirecrawlTool
User->>+Translator: Translate query to Spanish
Translator-->>FirecrawlTool: Spanish query
FirecrawlTool->>FirecrawlTool: Search ChileAtiende with Firecrawl API
FirecrawlTool-->>Translator: Spanish result
Translator-->>User: Translate result to English and display
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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 introduces two new Jupyter notebooks, expanding the examples/cookbooks directory with distinct AI agent functionalities: one for querying Chilean government services and another outlining a framework for automated cybersecurity Proof-of-Concept (PoC) search and validation. It's worth noting that the actual content of the pull request deviates from its stated title and description, which suggested a QA automation copilot.
Highlights
- Discrepancy in PR Content: It's important to note that the content of this pull request, which adds two distinct AI agent notebooks (Chilean government services and cybersecurity PoC), differs significantly from the pull request title and description, which refer to a 'PraisonAI QA Automation Copilot Notebook' for Gherkin, Selenium, and manual test case generation.
- New Notebook: Chile Government Services Assistant: A new Jupyter notebook (
Chile_Government_Services_Assistant.ipynb) has been added, demonstrating an AI chatbot that answers questions about Chilean government services. It leverages the Firecrawl API for searching anddeep-translatorfor Spanish-English translation. - New Notebook: Cybersecurity PoC Agent: Another new Jupyter notebook (
Pocky_Cybersecurity_PoC_Agent.ipynb) has been introduced. This notebook outlines a 'Pocky Query Tool' for automated CVE Proof-of-Concept (PoC) search and validation, usingexa-pyandopenai(with placeholder agent implementations for demonstration).
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Code Review
This pull request adds two new Jupyter notebooks as examples. The review identifies several areas for improvement, primarily related to security, code clarity, and correctness. Key issues include hardcoded API key placeholders, unused dependencies, and a misleading description in one of the notebooks. The suggested changes will improve the quality and user experience of these examples.
| "os.environ['FIRECRAWL_API_KEY'] = \"your api key here\"\n", | ||
| "os.environ['OPENAI_API_KEY'] = \"your api key here\"" |
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Hardcoding API key placeholders is a security risk. Load keys from a .env file using python-dotenv or from system environment variables. The suggested code uses os.getenv() as a safer alternative.
os.environ['FIRECRAWL_API_KEY'] = os.getenv("FIRECRAWL_API_KEY", "your api key here")
os.environ['OPENAI_API_KEY'] = os.getenv("OPENAI_API_KEY", "your api key here")
| "# Pocky Query Tool: Automated CVE PoC Search & Validation\n", | ||
| "\n", | ||
| "A lightweight, web-scale agent that helps you find, filter, and fetch real-world PoC exploits — so you don't have to.\n", | ||
| "\n", | ||
| "**Features:**\n", | ||
| "- Automatically searches multiple security-related websites\n", | ||
| "- Intelligently analyzes and extracts PoC code\n", | ||
| "- Automatically selects the most reliable PoC samples\n", | ||
| "- Supports collection of PoCs from multiple sources" |
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The notebook's description and title claim it's an "Automated CVE PoC Search & Validation" agent. However, the implementation consists of dummy classes and simulated logic. This is misleading. Update the notebook's description to clarify that it is a conceptual demonstration or a template, not a working tool.
| "os.environ[\"EXA_API_KEY\"] = \"your api key\"\n", | ||
| "os.environ[\"OPENAI_API_KEY\"] = \"your api key\"\n", | ||
| "os.environ[\"OPENAI_BASE_URL\"] = \"https://api.openai.com/v1\" # Optional, for custom OpenAI endpoints" |
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Hardcoding API key placeholders is a security risk. Load keys from a .env file using python-dotenv or from system environment variables. The suggested code uses os.getenv() as a safer alternative.
os.environ["EXA_API_KEY"] = os.getenv("EXA_API_KEY", "your api key")
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "your api key")
os.environ["OPENAI_BASE_URL"] = os.getenv("OPENAI_BASE_URL", "https://api.openai.com/v1") # Optional, for custom OpenAI endpoints
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "!pip install flask firecrawl praisonaiagents google-genai python-dotenv deep-translator" |
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The dependencies flask, praisonaiagents, and google-genai are installed but do not appear to be used in the notebook. To avoid confusion and keep the example focused, remove any unused packages from the installation command.
!pip install firecrawl python-dotenv deep-translator
| " self.instruction = instruction\n", | ||
| " self.template = template\n", | ||
| "\n", | ||
| " def search(self, search: str) -> str:\n", |
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The search method is type-hinted to return a str, but it can also return None. This can lead to TypeError exceptions if not handled correctly by the caller. Update the type hint to Optional[str] to accurately reflect the possible return values.
def search(self, search: str) -> Optional[str]:
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "!pip install praisonaiagents exa-py python-dotenv requests beautifulsoup4" |
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The dependencies praisonaiagents, requests, and beautifulsoup4 are installed but are not used in the notebook. To keep the example clean, remove any unused packages.
!pip install exa-py python-dotenv
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "## YAML Prompt (Validation Example)\n", |
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The markdown title is "YAML Prompt (Validation Example)", but the code cell below contains a multi-line string prompt for an LLM, not YAML content. Correct the title to accurately describe the content, for example, "LLM Prompt for Validation".
## LLM Prompt (Validation Example)\n
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
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Actionable comments posted: 3
🧹 Nitpick comments (2)
examples/cookbooks/Chile_Government_Services_Assistant.ipynb (1)
94-109: Improve error handling and logging in translation functions.The translation functions have basic error handling but could provide better user feedback and debugging information.
def translate_to_spanish(text): try: return GoogleTranslator(source='auto', target='es').translate(text) except Exception as e: - print("Translation to Spanish failed:", e) + print(f"Translation to Spanish failed for text '{text[:50]}...': {e}") return text def translate_to_english(text): try: # Remove Markdown images and None values before translation text = str(text).replace("None", "") text = re.sub(r'!\[.*?\]\(.*?\)', '', text) return GoogleTranslator(source='auto', target='en').translate(text) except Exception as e: - print("Translation to English failed:", e) + print(f"Translation to English failed for text '{str(text)[:50]}...': {e}") return textexamples/cookbooks/Pocky_Cybersecurity_PoC_Agent.ipynb (1)
176-177: Consider adding TODO comments for implementation guidance.The placeholder strings would benefit from more specific TODO comments to guide future implementation.
- description = f"Description for {cve_id} (replace with real Exa/OpenAI search)" - poc_sample = f"PoC code for {cve_id} (replace with real PoC search)" + # TODO: Implement real CVE description fetching using Exa API + description = f"Description for {cve_id} (replace with real Exa/OpenAI search)" + # TODO: Implement real PoC search across multiple security databases + poc_sample = f"PoC code for {cve_id} (replace with real PoC search)"
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🔇 Additional comments (8)
examples/cookbooks/Chile_Government_Services_Assistant.ipynb (4)
1-299: Inconsistency detected between PR objectives and actual content.The PR objectives describe adding a "PraisonAI QA Automation Copilot Notebook" for test automation, but this file implements a Chilean government services assistant. This appears to be a mismatch between the expected and actual content.
Likely an incorrect or invalid review comment.
129-169: Solid implementation with good error handling.The FirecrawlTool class is well-structured with proper validation, error handling, and filtering logic. The URL filtering for ChileAtiende pages and PDF exclusion is appropriate for the use case.
261-282: Robust main chat loop with good user experience.The chat loop handles user input well, includes proper exit conditions, and provides meaningful feedback when information isn't found. The bilingual approach with fallback to Spanish is user-friendly.
152-152: Verify URL filtering logic for robustness.The hardcoded URL filtering may break if the government website structure changes. Consider making the URL patterns configurable.
#!/bin/bash # Verify if ChileAtiende URLs follow the expected pattern curl -s "https://www.chileatiende.gob.cl" | grep -o 'https://www.chileatiende.gob.cl/fichas[^"]*' | head -10examples/cookbooks/Pocky_Cybersecurity_PoC_Agent.ipynb (4)
1-233: Content mismatch: This cybersecurity agent doesn't align with PR objectives.Similar to the other file, this cybersecurity PoC validation agent doesn't match the PR objectives describing a "PraisonAI QA Automation Copilot Notebook" for test automation.
Likely an incorrect or invalid review comment.
105-118: Dummy agent classes are appropriate for demo purposes.The minimal agent implementations are clearly marked as dummy/demo code and provide a good scaffold for understanding the intended workflow without overcomplicating the example.
139-153: Well-structured validation prompt with clear requirements.The validation prompt is comprehensive and provides clear guidance for the LLM, including specific output format requirements and reasoning expectations.
174-190: Main function demonstrates workflow effectively.The function provides a clear demonstration of the intended workflow stages and includes appropriate placeholder comments for where real implementation would go.
| "os.environ['FIRECRAWL_API_KEY'] = \"your api key here\"\n", | ||
| "os.environ['OPENAI_API_KEY'] = \"your api key here\"" |
There was a problem hiding this comment.
Security risk: Hardcoded API keys in example code.
The notebook contains placeholder API keys that could be accidentally committed with real values, leading to credential exposure.
Replace with secure environment variable handling:
-os.environ['FIRECRAWL_API_KEY'] = "your api key here"
-os.environ['OPENAI_API_KEY'] = "your api key here"
+# Load from .env file or set these environment variables securely
+# os.environ['FIRECRAWL_API_KEY'] = "your api key here"
+# os.environ['OPENAI_API_KEY'] = "your api key here"
+
+# Verify API keys are set
+if not os.environ.get('FIRECRAWL_API_KEY'):
+ raise ValueError("FIRECRAWL_API_KEY environment variable not set")
+if not os.environ.get('OPENAI_API_KEY'):
+ raise ValueError("OPENAI_API_KEY environment variable not set")📝 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['FIRECRAWL_API_KEY'] = \"your api key here\"\n", | |
| "os.environ['OPENAI_API_KEY'] = \"your api key here\"" | |
| # Load from .env file or set these environment variables securely | |
| # os.environ['FIRECRAWL_API_KEY'] = "your api key here" | |
| # os.environ['OPENAI_API_KEY'] = "your api key here" | |
| # Verify API keys are set | |
| if not os.environ.get('FIRECRAWL_API_KEY'): | |
| raise ValueError("FIRECRAWL_API_KEY environment variable not set") | |
| if not os.environ.get('OPENAI_API_KEY'): | |
| raise ValueError("OPENAI_API_KEY environment variable not set") |
🤖 Prompt for AI Agents
In examples/cookbooks/Chile_Government_Services_Assistant.ipynb at lines 69-70,
the code hardcodes API keys as string literals, posing a security risk. Remove
the hardcoded keys and instead instruct users to set these API keys securely in
their environment outside the notebook, such as through their system environment
variables or a secure secrets manager, and access them in the code using
environment variable reads without embedding actual keys.
| " data = json.loads(self.input_json)\n", | ||
| " return True if \"attack_intent\" in data and \"poc_sample\" in data else False\n", |
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🛠️ Refactor suggestion
Add error handling for JSON parsing.
The ValidationAgent.run() method lacks error handling for JSON parsing, which could cause runtime exceptions.
def run(self):
# Dummy validation logic for notebook demo
- data = json.loads(self.input_json)
- return True if "attack_intent" in data and "poc_sample" in data else False
+ try:
+ data = json.loads(self.input_json)
+ return True if "attack_intent" in data and "poc_sample" in data else False
+ except json.JSONDecodeError as e:
+ print(f"JSON parsing error: {e}")
+ return False📝 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.
| " data = json.loads(self.input_json)\n", | |
| " return True if \"attack_intent\" in data and \"poc_sample\" in data else False\n", | |
| def run(self): | |
| # Dummy validation logic for notebook demo | |
| try: | |
| data = json.loads(self.input_json) | |
| return True if "attack_intent" in data and "poc_sample" in data else False | |
| except json.JSONDecodeError as e: | |
| print(f"JSON parsing error: {e}") | |
| return False |
🤖 Prompt for AI Agents
In examples/cookbooks/Pocky_Cybersecurity_PoC_Agent.ipynb around lines 110 to
111, the code parses JSON without error handling, which may cause runtime
exceptions if the input is invalid. Wrap the json.loads call in a try-except
block to catch JSONDecodeError and handle it gracefully, such as returning False
or logging the error, to prevent the method from crashing.
| "os.environ[\"EXA_API_KEY\"] = \"your api key\"\n", | ||
| "os.environ[\"OPENAI_API_KEY\"] = \"your api key\"\n", | ||
| "os.environ[\"OPENAI_BASE_URL\"] = \"https://api.openai.com/v1\" # Optional, for custom OpenAI endpoints" |
There was a problem hiding this comment.
Security risk: Hardcoded API keys.
Same security issue as the other notebook - placeholder API keys that could be accidentally committed with real values.
Apply the same secure environment variable handling as recommended for the other notebook:
-os.environ["EXA_API_KEY"] = "your api key"
-os.environ["OPENAI_API_KEY"] = "your api key"
-os.environ["OPENAI_BASE_URL"] = "https://api.openai.com/v1" # Optional, for custom OpenAI endpoints
+# Load from .env file or set these environment variables securely
+# os.environ["EXA_API_KEY"] = "your api key"
+# os.environ["OPENAI_API_KEY"] = "your api key"
+# os.environ["OPENAI_BASE_URL"] = "https://api.openai.com/v1"
+
+# Verify API keys are set
+if not os.environ.get('EXA_API_KEY'):
+ raise ValueError("EXA_API_KEY environment variable not set")
+if not os.environ.get('OPENAI_API_KEY'):
+ raise ValueError("OPENAI_API_KEY environment variable not set")📝 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[\"EXA_API_KEY\"] = \"your api key\"\n", | |
| "os.environ[\"OPENAI_API_KEY\"] = \"your api key\"\n", | |
| "os.environ[\"OPENAI_BASE_URL\"] = \"https://api.openai.com/v1\" # Optional, for custom OpenAI endpoints" | |
| # Load from .env file or set these environment variables securely | |
| # os.environ["EXA_API_KEY"] = "your api key" | |
| # os.environ["OPENAI_API_KEY"] = "your api key" | |
| # os.environ["OPENAI_BASE_URL"] = "https://api.openai.com/v1" | |
| # Verify API keys are set | |
| if not os.environ.get('EXA_API_KEY'): | |
| raise ValueError("EXA_API_KEY environment variable not set") | |
| if not os.environ.get('OPENAI_API_KEY'): | |
| raise ValueError("OPENAI_API_KEY environment variable not set") |
🤖 Prompt for AI Agents
In examples/cookbooks/Pocky_Cybersecurity_PoC_Agent.ipynb around lines 78 to 80,
the API keys are hardcoded as string literals, posing a security risk. Remove
these hardcoded keys and instead instruct users to set the API keys securely
outside the code, such as in environment variables or a secure secrets manager.
Update the code to read the API keys from environment variables without
assigning them directly in the notebook.
There was a problem hiding this comment.
Actionable comments posted: 2
🧹 Nitpick comments (3)
examples/cookbooks/Government-Services-Assistant.ipynb (3)
47-47: Remove unused dependencies.The installation includes
flaskandpython-dotenvpackages that don't appear to be used anywhere in the notebook. Consider removing them to reduce dependency bloat.-"!pip install flask firecrawl praisonaiagents google-genai python-dotenv deep-translator" +"!pip install firecrawl praisonaiagents google-genai deep-translator"
138-139: Reconsider the arbitrary minimum query length.The 5-character minimum for search queries seems arbitrary and might reject valid short queries like "RUT" or "ID".
- if not search or len(search) < 5: - return "Error: Please provide a valid search query (at least 5 characters)." + if not search or len(search.strip()) < 2: + return "Error: Please provide a valid search query."
150-153: URL filtering might be too restrictive.The filtering only includes URLs starting with
https://www.chileatiende.gob.cl/fichaswhich might exclude other relevant government service pages.Consider broadening the filter to include more ChileAtiende pages:
- if str(result.get("url", "")).startswith("https://www.chileatiende.gob.cl/fichas") and not str(result.get("url", "")).endswith("pdf") + if "chileatiende.gob.cl" in str(result.get("url", "")) and not str(result.get("url", "")).endswith("pdf")
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- GitHub Check: test-examples
- GitHub Check: test-core (3.11)
- GitHub Check: Run tests and collect coverage
🔇 Additional comments (5)
examples/cookbooks/Government-Services-Assistant.ipynb (5)
94-109: Well-implemented translation functions with good error handling.The translation functions properly handle exceptions and the English translation function includes smart preprocessing to remove markdown images and None values before translation.
188-201: Clean and well-structured template definition.The markdown template is properly formatted with clear placeholders and good structure for displaying search results.
221-225: Proper tool initialization.The FirecrawlTool is correctly initialized using environment variables and the defined constants.
261-282: Well-implemented interactive chat loop.The chat loop properly handles user interaction, translation workflow, and error cases. The bilingual approach (translating queries to Spanish for better search results, then translating responses back to English) is well-designed for this use case.
1-29: Critical mismatch between PR objectives and actual content.The PR objectives describe this as a "PraisonAI QA Automation Copilot Notebook" for generating Gherkin features, Selenium scripts, and manual test cases. However, the actual notebook is a Chilean government services assistant chatbot using Firecrawl API.
Additionally, the Colab badge URL (line 27) references
Chile_Government_Services_Assistant.ipynbbut the actual filename isGovernment-Services-Assistant.ipynb.Apply this diff to fix the URL:
-"[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Chile_Government_Services_Assistant.ipynb)\n" +"[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Government-Services-Assistant.ipynb)\n"Likely an incorrect or invalid review comment.
| "import os\n", | ||
| "\n", | ||
| "os.environ['FIRECRAWL_API_KEY'] = \"your api key here\"\n", | ||
| "os.environ['OPENAI_API_KEY'] = \"your api key here\"" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Remove unused OPENAI_API_KEY.
The OPENAI_API_KEY is set but never used anywhere in the notebook. This creates confusion and unnecessary dependency.
-"os.environ['OPENAI_API_KEY'] = \"your api key here\""📝 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 here\"" |
🤖 Prompt for AI Agents
In examples/cookbooks/Government-Services-Assistant.ipynb at line 70, the
OPENAI_API_KEY environment variable is set but not used anywhere in the
notebook. Remove this line entirely to eliminate confusion and unnecessary
dependency on the API key.
| "os.environ['FIRECRAWL_API_KEY'] = \"your api key here\"\n", | ||
| "os.environ['OPENAI_API_KEY'] = \"your api key here\"" |
There was a problem hiding this comment.
Improve API key security practices.
Using hardcoded placeholder strings for API keys can lead users to accidentally commit real keys to version control. Consider using more secure alternatives.
Apply this diff to improve security:
-"os.environ['FIRECRAWL_API_KEY'] = \"your api key here\"\n",
-"os.environ['OPENAI_API_KEY'] = \"your api key here\""
+"# Set your API keys here (never commit real keys to version control)\n",
+"from getpass import getpass\n",
+"os.environ['FIRECRAWL_API_KEY'] = getpass('Enter your Firecrawl API key: ')\n",
+"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.
| "os.environ['FIRECRAWL_API_KEY'] = \"your api key here\"\n", | |
| "os.environ['OPENAI_API_KEY'] = \"your api key here\"" | |
| "# Set your API keys here (never commit real keys to version control)\n", | |
| "from getpass import getpass\n", | |
| "os.environ['FIRECRAWL_API_KEY'] = getpass('Enter your Firecrawl API key: ')\n", | |
| "os.environ['OPENAI_API_KEY'] = getpass('Enter your OpenAI API key: ')" |
🤖 Prompt for AI Agents
In examples/cookbooks/Government-Services-Assistant.ipynb around lines 69 to 70,
the API keys are set using hardcoded placeholder strings, which risks accidental
commits of real keys. Replace these hardcoded assignments with instructions or
code to load the API keys securely from environment variables or external
configuration files, and remove any direct assignment of keys in the code to
improve security practices.
This notebook introduces the PraisonAI QA Automation Copilot, an intelligent agent designed to automate and streamline the generation of QA artifacts using advanced LLMs via PraisonAI. The agent leverages the same prompt structure and routing logic as our backend, ensuring consistency and reliability in test generation.
Key Features:
Gherkin Feature Generation: Automatically creates Gherkin feature files from user stories or requirements.
Selenium Script Generation: Produces ready-to-use Selenium test scripts in Python based on functional requirements.
Manual Test Case Generation: Generates detailed manual test cases from user stories, including steps, expected results, and priorities.
QA Chat Assistance: Provides clear explanations and best practices for QA and testing-related questions.
Summary by CodeRabbit