Add Qwen2.5 Instruction Agent notebook using Hugging Face Transformers #606
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WalkthroughThree new Jupyter notebooks are introduced as examples. One demonstrates building an AI-powered code analysis agent that generates structured quality reports from code repositories. Another implements a multi-agent workflow for predictive maintenance, simulating sensor data collection, anomaly detection, failure prediction, and maintenance scheduling using coordinated AI agents. The third showcases a simple chat interaction with the Qwen2.5-0.5B-Instruct language model using Hugging Face Transformers. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant CodeAnalysisAgent
participant GitIngest
participant OpenAI
User->>CodeAnalysisAgent: Provide code source (URL or path)
CodeAnalysisAgent->>GitIngest: Ingest and summarize repository
GitIngest-->>CodeAnalysisAgent: Return code summary and structure
CodeAnalysisAgent->>OpenAI: Submit analysis context and task
OpenAI-->>CodeAnalysisAgent: Return structured code analysis report
CodeAnalysisAgent-->>User: Display code analysis report
sequenceDiagram
participant User
participant SensorAgent
participant PerformanceAgent
participant AnomalyAgent
participant FailureAgent
participant MaintenanceAgent
User->>SensorAgent: Start workflow
SensorAgent->>PerformanceAgent: Provide sensor data
PerformanceAgent->>AnomalyAgent: Provide performance metrics
AnomalyAgent->>FailureAgent: Provide anomaly results
FailureAgent->>MaintenanceAgent: Provide failure predictions
MaintenanceAgent-->>User: Output maintenance schedule and status
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Hello @DhivyaBharathy-web, 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!
Summary of Changes
Hello team, gemini-code-assist here to provide a summary of this pull request. This PR introduces two new example Jupyter notebooks showcasing different applications of multi-agent workflows using the praisonaiagents library. One notebook demonstrates a code analysis agent, and the other illustrates a predictive maintenance workflow.
Note: The PR title and description mention adding a Qwen2.5 Instruction Agent notebook using Hugging Face Transformers, but the files included in the patch are for a Code Analysis Agent and a Predictive Maintenance workflow. This summary focuses on the changes present in the patch.
Highlights
- New Example Notebooks: Adds two new Jupyter notebooks (
.ipynb) to theexamples/cookbooksdirectory. - Code Analysis Agent: Introduces a notebook demonstrating how to set up a single agent using
praisonaiagentsandgitingestto analyze code from a repository or path, defining a structured output using Pydantic. - Predictive Maintenance Workflow: Adds a notebook showcasing a multi-agent workflow for predictive maintenance, chaining together agents for data collection, performance analysis, anomaly detection, failure prediction, and maintenance scheduling.
- Multi-Agent Workflow Examples: Provides practical examples of defining agents, tasks, linking tasks, and running workflows with
praisonaiagents.
Changelog
- examples/cookbooks/Code_Analysis_Agent.ipynb
- Added a new Jupyter notebook for a Code Analysis Agent.
- Includes sections for dependencies (
praisonaiagents,gitingest), setting API key, defining Pydantic models for output (CodeMetrics,CodeAnalysisReport), setting up the agent and task, a main function to ingest and analyze code, and example output.
- examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb
- Added a new Jupyter notebook for a Predictive Maintenance Multi-Agent Workflow.
- Includes sections for dependencies (
praisonaiagents), setting API key, defining helper functions for simulating workflow steps (collecting data, analyzing performance, detecting anomalies, predicting failures, scheduling maintenance), defining multiple agents and chained tasks, and running the asynchronous workflow.
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Two notebooks arrive,
Agents working, quite alive,
Code checked, machines thrive.
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Code Review
This pull request introduces two new example Jupyter notebooks: Code_Analysis_Agent.ipynb and Predictive_Maintenance_Multi_Agent_Workflow.ipynb. Both notebooks serve as valuable cookbooks for users of PraisonAIAgents, demonstrating different use cases and agent setups.
The code within the Python cells generally adheres to PEP 8 naming conventions and practices. Markdown cells are used effectively for documentation.
My review focuses on the two patched files. The PR title and user description mention a Qwen2.5 Instruction Agent notebook, which I assume is the Qwen2_5_InstructionAgent.ipynb listed as an "Additional file" but not included in the provided patches for review. The reviewed notebooks primarily use OpenAI.
Several areas for improvement have been identified, mainly concerning documentation clarity, example completeness, and robustness of certain notebook elements (like URLs and directory navigation). Addressing these will enhance the usability and reliability of these examples for the community.
No specific style guide was provided for this review. Therefore, feedback related to Python code style is based on PEP 8, and feedback on Markdown content is based on general best practices for clarity and readability.
Summary of Findings
- Colab Badge URL Issues: Both notebooks have Colab badge URLs pointing to a fork (
DhivyaBharathy-web/PraisonAI). Additionally,Predictive_Maintenance_Multi_Agent_Workflow.ipynbhas a filename typo in its badge URL. These should be corrected to point to the main repository and the correct filenames. - API Key Management Guidance: Both notebooks use API key placeholders, which is good. Adding a note on secure API key management best practices would be beneficial for users.
- Example Completeness in
Code_Analysis_Agent.ipynb: TheCode_Analysis_Agent.ipynbdefines ananalyze_codefunction but its "Output" cell only shows hardcoded example output. Including a (possibly commented-out) example of actually calling this function would make the notebook more complete. - Directory Navigation Robustness: The
%cd PraisonAIcommand inCode_Analysis_Agent.ipynbmight not be robust across all user environments. Clarifying its context or purpose is recommended. - Typing for Dictionaries in Helper Functions (Low Severity): In
Predictive_Maintenance_Multi_Agent_Workflow.ipynb, helper functions (lines 129, 139, 150) use genericDicttype hints. While acceptable for a cookbook, usingTypedDictor Pydantic models for these dictionary structures would improve code clarity and maintainability. (Not commented inline due to review settings). - Minor Typo in Example Output (Low Severity): In
Predictive_Maintenance_Multi_Agent_Workflow.ipynb(line 300), the hardcoded example output string ends with--------------------------------------------------]. The trailing]appears to be a typo. (Not commented inline due to review settings).
Merge Readiness
This pull request adds two helpful example notebooks. However, there are a few issues that should be addressed before merging to ensure clarity, correctness, and usability for the community. The most critical is the broken Colab badge link in the Predictive_Maintenance_Multi_Agent_Workflow.ipynb. Other suggestions relate to improving API key guidance, example completeness, and URL consistency for Colab badges.
I am unable to approve pull requests. Please have another reviewer approve these changes after the suggested modifications are made.
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi-Agent_Workflow.ipynb)" |
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There appear to be two issues with the Colab badge URL:
- Filename Mismatch: The URL uses
Predictive_Maintenance_Multi-Agent_Workflow.ipynb(with a hyphen inMulti-Agent), but the actual notebook filename isPredictive_Maintenance_Multi_Agent_Workflow.ipynb(no hyphen inMulti_Agent). This will result in a 404 error. - Repository Pointer: The URL points to the
DhivyaBharathy-web/PraisonAIfork. Should this be updated to the mainMervinPraison/PraisonAIrepository for the official example?
Consider updating the URL to correctly reflect the filename and potentially the main repository.
[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb)
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Code_Analysis_Agent.ipynb)\n" |
There was a problem hiding this comment.
The Colab badge URL currently points to a fork (DhivyaBharathy-web/PraisonAI). For consistency and to ensure users access the canonical version, should this URL be updated to point to the main repository (MervinPraison/PraisonAI) once merged?
If so, the URL would be: https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Code_Analysis_Agent.ipynb (assuming main is the target branch).
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = 'your_api_key_here'" |
There was a problem hiding this comment.
Using a placeholder like 'your_api_key_here' for the OPENAI_API_KEY is good practice for example notebooks. To further help users, especially those new to API key management, could we consider adding a brief note (e.g., in a preceding markdown cell or as a code comment) about securely managing API keys?
This note could suggest using environment variables (perhaps loaded from a .env file for local setups), Colab secrets, or other secrets management tools, and explicitly warn against committing actual API keys to version control. This would promote safer practices among users adapting this cookbook.
| "import json\n", | ||
| "from IPython.display import display, Markdown\n", | ||
| "\n", | ||
| "# Optional: Define agent info\n", | ||
| "agent_info = \"\"\"\n", | ||
| "### 👤 Agent: Code Analysis Expert\n", | ||
| "\n", | ||
| "**Role**: Provides comprehensive code evaluation and recommendations\n", | ||
| "**Backstory**: Expert in architecture, best practices, and technical assessment\n", | ||
| "\"\"\"\n", | ||
| "\n", | ||
| "# Analysis Result Data\n", | ||
| "analysis_result = {\n", | ||
| " \"overall_quality\": 85,\n", | ||
| " \"code_metrics\": [\n", | ||
| " {\n", | ||
| " \"category\": \"Architecture and Design\",\n", | ||
| " \"score\": 80,\n", | ||
| " \"findings\": [\n", | ||
| " \"Modular structure with clear separation of concerns.\",\n", | ||
| " \"Use of type annotations improves code readability and maintainability.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Code Maintainability\",\n", | ||
| " \"score\": 85,\n", | ||
| " \"findings\": [\n", | ||
| " \"Consistent use of type hints and NamedTuple for structured data.\",\n", | ||
| " \"Logical organization of functions and classes.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Performance Optimization\",\n", | ||
| " \"score\": 75,\n", | ||
| " \"findings\": [\n", | ||
| " \"Potential performance overhead due to repeated sys.stdout.write calls.\",\n", | ||
| " \"Efficient use of optional parameters to control execution flow.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Security Practices\",\n", | ||
| " \"score\": 80,\n", | ||
| " \"findings\": [\n", | ||
| " \"No obvious security vulnerabilities in the code.\",\n", | ||
| " \"Proper encapsulation of functionality.\"\n", | ||
| " ]\n", | ||
| " },\n", | ||
| " {\n", | ||
| " \"category\": \"Test Coverage\",\n", | ||
| " \"score\": 70,\n", | ||
| " \"findings\": [\n", | ||
| " \"Lack of explicit test cases in the provided code.\",\n", | ||
| " \"Use of type checking suggests some level of validation.\"\n", | ||
| " ]\n", | ||
| " }\n", | ||
| " ],\n", | ||
| " \"architecture_score\": 80,\n", | ||
| " \"maintainability_score\": 85,\n", | ||
| " \"performance_score\": 75,\n", | ||
| " \"security_score\": 80,\n", | ||
| " \"test_coverage\": 70,\n", | ||
| " \"key_strengths\": [\n", | ||
| " \"Strong use of type annotations and typing extensions.\",\n", | ||
| " \"Clear separation of CLI argument parsing and business logic.\"\n", | ||
| " ],\n", | ||
| " \"improvement_areas\": [\n", | ||
| " \"Increase test coverage to ensure robustness.\",\n", | ||
| " \"Optimize I/O operations to improve performance.\"\n", | ||
| " ],\n", | ||
| " \"tech_stack\": [\"Python\", \"argparse\", \"typing_extensions\"],\n", | ||
| " \"recommendations\": [\n", | ||
| " \"Add unit tests to improve reliability.\",\n", | ||
| " \"Consider async I/O for improved performance in CLI tools.\"\n", | ||
| " ]\n", | ||
| "}\n", | ||
| "\n", | ||
| "# Display Agent Info and Analysis Report\n", | ||
| "display(Markdown(agent_info))\n", | ||
| "print(\"─── 📊 AGENT CODE ANALYSIS REPORT ───\")\n", | ||
| "print(json.dumps(analysis_result, indent=4))\n" |
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This cell effectively demonstrates the expected output structure using hardcoded agent_info and analysis_result. To make the cookbook more illustrative of the complete workflow, would it be beneficial to include an example of how to call the analyze_code function (defined in a previous cell) and display its dynamic output?
This could be a commented-out code block to prevent long execution times by default but still show users how to run the analysis themselves. For instance:
# # Example of actually running the analysis (replace with a real URL or path):
# try:
# # Use a small, publicly accessible repository for a quick example if possible
# repo_to_analyze = "https://github.com/MervinPraison/PraisonAI" # Example, adjust as needed
# print(f"\nAttempting to analyze: {repo_to_analyze}\n")
# actual_report = analyze_code(repo_to_analyze)
# print("\n─── 📊 ACTUAL CODE ANALYSIS REPORT (from analyze_code function) ───")
# # Assuming analyze_code returns a Pydantic model, use .model_dump()
# print(json.dumps(actual_report.model_dump(), indent=4) if hasattr(actual_report, 'model_dump') else json.dumps(actual_report, indent=4))
# except Exception as e:
# print(f"\nError running live analysis: {e}")
# print("Displaying pre-canned example output instead.")This would provide a clearer connection between the function definition and its usage.
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "%cd PraisonAI" |
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The magic command %cd PraisonAI assumes a specific directory structure where PraisonAI is a direct subdirectory of the current working directory. This might not hold true in all environments where the notebook is run (e.g., if cloned to a different depth or if PraisonAI is installed as a package).
Could we add a comment clarifying the purpose of this cell and its expected context (e.g., "Run this cell if you've cloned the PraisonAI repository and are running this notebook from the root of the cloned repo in Colab")? Or, if this is essential for relative imports within the notebook's context that might be part of PraisonAI itself, perhaps explore more robust path handling if feasible for a cookbook.
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = 'enter your api key'" |
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Similar to the other notebook, it's great that a placeholder 'enter your api key' is used. To enhance user guidance on security, could we add a brief note about best practices for API key management?
Suggestions include using environment variables, .env files for local development, or Colab secrets, and a warning against committing real keys. This helps users adopt secure habits when they adapt this example.
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
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Actionable comments posted: 4
♻️ Duplicate comments (1)
examples/cookbooks/Code_Analysis_Agent.ipynb (1)
67-67: Security risk: Hardcoded API key placeholder.Same issue as the other notebook - the API key handling should be more secure.
🧹 Nitpick comments (6)
examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb (3)
114-120: Improve sensor data simulation for more realistic variability.The current time-based modulo approach creates predictable patterns. Consider using random values for more realistic sensor simulation.
+import random def collect_sensor_data(): return { - "temperature": 75 + (int(time.time()) % 20), - "vibration": 0.5 + (int(time.time()) % 10) / 10, - "pressure": 100 + (int(time.time()) % 50), - "noise_level": 60 + (int(time.time()) % 30) + "temperature": 75 + random.randint(0, 20), + "vibration": 0.5 + random.random(), + "pressure": 100 + random.randint(0, 50), + "noise_level": 60 + random.randint(0, 30) }
129-137: Consider making anomaly thresholds configurable.The hardcoded thresholds (90 for temperature, 1.2 for vibration, 0.85 for efficiency) should be configurable parameters for better flexibility.
-def detect_anomalies(sensor_data: Dict, performance: Dict): +def detect_anomalies(sensor_data: Dict, performance: Dict, thresholds: Dict = None): + if thresholds is None: + thresholds = { + "temperature_max": 90, + "vibration_max": 1.2, + "efficiency_min": 0.85 + } + anomalies = [] - if sensor_data["temperature"] > 90: + if sensor_data["temperature"] > thresholds["temperature_max"]: anomalies.append({"type": "temperature_high", "severity": "critical"}) - if sensor_data["vibration"] > 1.2: + if sensor_data["vibration"] > thresholds["vibration_max"]: anomalies.append({"type": "vibration_excess", "severity": "warning"}) - if performance["efficiency"] < 0.85: + if performance["efficiency"] < thresholds["efficiency_min"]: anomalies.append({"type": "efficiency_low", "severity": "warning"}) return anomalies
237-301: Sample output should be moved to markdown or documentation.The large hardcoded output block in a code cell makes the notebook cluttered. Consider moving this to a markdown cell or removing it entirely since it will be generated when the notebook runs.
Convert this code cell to a markdown cell or remove it since the actual output will be generated when users run the workflow.
examples/cookbooks/Code_Analysis_Agent.ipynb (3)
334-414: Sample output data should be generated dynamically.The hardcoded sample output makes the notebook less educational. Users should see the actual agent execution rather than static data.
Consider replacing the hardcoded output with an actual call to
analyze_code()with a sample repository, or move this to a markdown cell as an example.
421-421: Remove or explain the directory change command.The
%cd PraisonAIcommand at the end seems unrelated to the notebook's purpose and may cause confusion.Either remove this command or add a comment explaining why it's needed:
+# Change to PraisonAI directory for further examples %cd PraisonAI
392-394: Python version mismatch in metadata.The notebook metadata shows Python 3.10, but the other notebook shows Python 3.9. Consider standardizing the Python version across examples.
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🔇 Additional comments (4)
examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb (2)
1-12: Inconsistency between PR objectives and actual content.The PR objectives mention adding a "Qwen2.5 Instruction Agent notebook using Hugging Face Transformers", but this file demonstrates a predictive maintenance workflow using PraisonAIAgents. This suggests either the PR description is incorrect or the wrong files were submitted.
Likely an incorrect or invalid review comment.
188-192:Details
✅ Verification successful
Review task configuration for potential execution issues.
The task configuration uses conditional branching with a dictionary condition, but the logic may not work as expected. Verify that the PraisonAIAgents framework supports this syntax.
Run this script to verify the task configuration syntax:
🏁 Script executed:
#!/bin/bash # Description: Search for similar task configuration patterns in the codebase # to verify if the condition syntax is supported rg -A 5 -B 5 "task_type.*decision" --type py rg -A 5 -B 5 "condition.*:" --type pyLength of output: 67428
Conditional branching with
conditiondict is supportedI’ve confirmed that PraisonAIAgents’
Task(..., task_type="decision", condition={...})syntax—with both list and string values—is used throughout the codebase (e.g.examples/python/usecases/predictive-maintenance.pyand numerous tests insrc/praisonai-agents/tests/). No changes are needed here.examples/cookbooks/Code_Analysis_Agent.ipynb (2)
1-12: Inconsistency between PR objectives and actual content.Similar to the other file, this notebook demonstrates code analysis functionality, not the Qwen2.5 Instruction Agent mentioned in the PR objectives.
Likely an incorrect or invalid review comment.
95-116: Well-structured Pydantic models with comprehensive coverage.The data models are well-designed and cover all important aspects of code analysis including metrics, security, and documentation quality.
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = 'enter your api key'" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Security risk: Hardcoded API key placeholder.
The code shows a placeholder API key that users need to replace. Consider adding a warning comment or using a more secure approach like prompting for the key.
import os
-os.environ['OPENAI_API_KEY'] = 'enter your api key'
+# WARNING: Replace with your actual OpenAI API key
+# For production, use environment variables or secure key management
+os.environ['OPENAI_API_KEY'] = 'your_openai_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.
| "import os\n", | |
| "os.environ['OPENAI_API_KEY'] = 'enter your api key'" | |
| import os | |
| # WARNING: Replace with your actual OpenAI API key | |
| # For production, use environment variables or secure key management | |
| os.environ['OPENAI_API_KEY'] = 'your_openai_api_key_here' |
🤖 Prompt for AI Agents
In examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb around
lines 65 to 66, the API key is hardcoded as a placeholder string, which poses a
security risk. Replace the hardcoded key with a prompt that securely asks the
user to input their API key at runtime, or add a clear warning comment
instructing users not to hardcode their keys and to use environment variables or
secure input methods instead.
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi-Agent_Workflow.ipynb)" |
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Fix the Colab badge URL path.
The Colab badge URL references a file with a hyphen (Predictive_Maintenance_Multi-Agent_Workflow.ipynb) but the actual filename uses an underscore (Predictive_Maintenance_Multi_Agent_Workflow.ipynb).
Apply this fix:
-[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi-Agent_Workflow.ipynb)
+[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb)📝 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.
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi-Agent_Workflow.ipynb)" | |
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb)" |
🤖 Prompt for AI Agents
In examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb at line
18, the Colab badge URL incorrectly uses a hyphen in the filename instead of the
correct underscores. Update the URL path in the badge markdown to replace the
hyphen with underscores so it matches the actual filename
Predictive_Maintenance_Multi_Agent_Workflow.ipynb.
| " Analyze code from directory path or GitHub URL\n", | ||
| " \"\"\"\n", | ||
| " # Ingest code content\n", | ||
| " summary, tree, content = ingest(code_source)\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling for code ingestion.
The ingest(code_source) call could fail for invalid URLs or paths. Consider adding error handling.
def analyze_code(code_source: str) -> CodeAnalysisReport:
"""
Analyze code from directory path or GitHub URL
"""
- # Ingest code content
- summary, tree, content = ingest(code_source)
+ # Ingest code content with error handling
+ try:
+ summary, tree, content = ingest(code_source)
+ except Exception as e:
+ raise ValueError(f"Failed to ingest code from {code_source}: {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.
| " summary, tree, content = ingest(code_source)\n", | |
| def analyze_code(code_source: str) -> CodeAnalysisReport: | |
| """ | |
| Analyze code from directory path or GitHub URL | |
| """ | |
| # Ingest code content with error handling | |
| try: | |
| summary, tree, content = ingest(code_source) | |
| except Exception as e: | |
| raise ValueError(f"Failed to ingest code from {code_source}: {e}") | |
| # …rest of the function… |
🤖 Prompt for AI Agents
In examples/cookbooks/Code_Analysis_Agent.ipynb at line 196, the call to
ingest(code_source) lacks error handling, which may cause the program to crash
if the input URL or path is invalid. Wrap the ingest call in a try-except block
to catch potential exceptions, and handle errors gracefully by logging an error
message or providing fallback behavior.
| "def analyze_code(code_source: str) -> CodeAnalysisReport:\n", | ||
| " \"\"\"\n", | ||
| " Analyze code from directory path or GitHub URL\n", | ||
| " \"\"\"\n", | ||
| " # Ingest code content\n", | ||
| " summary, tree, content = ingest(code_source)\n", | ||
| "\n", | ||
| " # Concatenate context into structured format\n", | ||
| " context_text = f\"\"\"\n", | ||
| " CODE REPOSITORY ANALYSIS\n", | ||
| " =======================\n", | ||
| "\n", | ||
| " SUMMARY\n", | ||
| " -------\n", | ||
| " {summary}\n", | ||
| "\n", | ||
| " REPOSITORY STRUCTURE\n", | ||
| " -------------------\n", | ||
| " {tree}\n", | ||
| "\n", | ||
| " SOURCE CODE\n", | ||
| " -----------\n", | ||
| " {content}\n", | ||
| " \"\"\"\n", | ||
| "\n", | ||
| " # Initialize and run analysis\n", | ||
| " agents = PraisonAIAgents(\n", | ||
| " agents=[code_analyzer],\n", | ||
| " tasks=[code_analysis_task]\n", | ||
| " )\n", | ||
| "\n", | ||
| " return agents.start(context_text)" |
There was a problem hiding this comment.
💡 Verification agent
🧩 Analysis chain
Potential issue with return value in analyze_code function.
The function calls agents.start(context_text) but it's unclear if this returns a CodeAnalysisReport object as expected by the function signature. The return type annotation suggests it should return the Pydantic model.
Check how the PraisonAIAgents.start() method works and what it returns:
🏁 Script executed:
#!/bin/bash
# Description: Verify the return type of PraisonAIAgents.start() method
rg -A 10 -B 5 "def start" --type py
ast-grep --pattern 'def start($ARGS):
$$$'Length of output: 14741
🏁 Script executed:
#!/bin/bash
# Locate the definition of PraisonAIAgents and its `start` method
rg -n "class PraisonAIAgents" .Length of output: 336
🏁 Script executed:
#!/bin/bash
# Display PraisonAIAgents class definition and its start method
rg -A 30 -B 5 "class PraisonAIAgents" src/praisonai-agents/praisonaiagents/agents/agents.pyLength of output: 1669
🏁 Script executed:
#!/bin/bash
# Find and display the `start` method implementation in PraisonAIAgents
rg -n "def start" src/praisonai-agents/praisonaiagents/agents/agents.py
rg -A20 -B2 "def start" src/praisonai-agents/praisonaiagents/agents/agents.pyLength of output: 1109
🏁 Script executed:
#!/bin/bash
# Display full implementation of PraisonAIAgents.start() including return statements
rg -A100 -B2 "def start" src/praisonai-agents/praisonaiagents/agents/agents.pyLength of output: 4101
🏁 Script executed:
#!/bin/bash
# Locate get_task_result implementation
rg -n "def get_task_result" src/praisonai-agents/praisonaiagents/agents/agents.py
rg -A20 -B2 "def get_task_result" src/praisonai-agents/praisonaiagents/agents/agents.pyLength of output: 1024
Ensure analyze_code Returns a CodeAnalysisReport
The PraisonAIAgents.start() method returns either a raw string (the last task’s .raw) or, when called with return_dict=True, a dict of statuses/results—not a Pydantic CodeAnalysisReport. To match your function signature, you should:
• Call start with return_dict=True
• Parse its output into your CodeAnalysisReport model
Locations to update:
- examples/cookbooks/Code_Analysis_Agent.ipynb, around lines 191–222
Suggested change:
- return agents.start(context_text)
+ # Run agents and get full results dict
+ raw_result = agents.start(context_text, return_dict=True)
+ # Convert to Pydantic model
+ return CodeAnalysisReport.parse_obj(raw_result)📝 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 analyze_code(code_source: str) -> CodeAnalysisReport:\n", | |
| " \"\"\"\n", | |
| " Analyze code from directory path or GitHub URL\n", | |
| " \"\"\"\n", | |
| " # Ingest code content\n", | |
| " summary, tree, content = ingest(code_source)\n", | |
| "\n", | |
| " # Concatenate context into structured format\n", | |
| " context_text = f\"\"\"\n", | |
| " CODE REPOSITORY ANALYSIS\n", | |
| " =======================\n", | |
| "\n", | |
| " SUMMARY\n", | |
| " -------\n", | |
| " {summary}\n", | |
| "\n", | |
| " REPOSITORY STRUCTURE\n", | |
| " -------------------\n", | |
| " {tree}\n", | |
| "\n", | |
| " SOURCE CODE\n", | |
| " -----------\n", | |
| " {content}\n", | |
| " \"\"\"\n", | |
| "\n", | |
| " # Initialize and run analysis\n", | |
| " agents = PraisonAIAgents(\n", | |
| " agents=[code_analyzer],\n", | |
| " tasks=[code_analysis_task]\n", | |
| " )\n", | |
| "\n", | |
| " return agents.start(context_text)" | |
| def analyze_code(code_source: str) -> CodeAnalysisReport: | |
| """ | |
| Analyze code from directory path or GitHub URL | |
| """ | |
| # Ingest code content | |
| summary, tree, content = ingest(code_source) | |
| # Concatenate context into structured format | |
| context_text = f""" | |
| CODE REPOSITORY ANALYSIS | |
| ======================= | |
| SUMMARY | |
| ------- | |
| {summary} | |
| REPOSITORY STRUCTURE | |
| ------------------- | |
| {tree} | |
| SOURCE CODE | |
| ----------- | |
| {content} | |
| """ | |
| # Initialize and run analysis | |
| agents = PraisonAIAgents( | |
| agents=[code_analyzer], | |
| tasks=[code_analysis_task] | |
| ) | |
| # Run agents and get full results dict | |
| raw_result = agents.start(context_text, return_dict=True) | |
| # Convert to Pydantic model | |
| return CodeAnalysisReport.parse_obj(raw_result) |
🤖 Prompt for AI Agents
In examples/cookbooks/Code_Analysis_Agent.ipynb around lines 191 to 222, the
analyze_code function currently returns the result of
agents.start(context_text), which does not return a CodeAnalysisReport object as
expected. To fix this, modify the call to agents.start by passing
return_dict=True, then parse the returned dictionary into a CodeAnalysisReport
instance before returning it. This ensures the function's return type matches
its annotation.
There was a problem hiding this comment.
Actionable comments posted: 2
🧹 Nitpick comments (1)
examples/cookbooks/Qwen2_5_InstructionAgent.ipynb (1)
351-359: Add error handling for model generation.Consider adding error handling for common issues like GPU memory limitations or generation failures to improve the user experience.
+try: outputs = model.generate(**inputs, max_new_tokens=100) +except torch.cuda.OutOfMemoryError: + print("GPU memory error. Try reducing max_new_tokens or using CPU.") + raise +except Exception as e: + print(f"Generation failed: {e}") + raise
📜 Review details
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Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
examples/cookbooks/Qwen2_5_InstructionAgent.ipynb(1 hunks)
🔇 Additional comments (4)
examples/cookbooks/Qwen2_5_InstructionAgent.ipynb (4)
75-76: LGTM! Clean dependency installation.The installation commands are correct and use the
--quietflag appropriately to reduce output noise.
99-100: LGTM! Correct imports for the use case.The imports are appropriate and minimal - exactly what's needed for loading and using the Qwen model.
342-349: LGTM! Good model loading practices.The model loading code follows best practices by using
torch_dtype="auto"anddevice_map="auto"for optimal performance and memory management.
401-402: LGTM! Correct output handling.The response decoding is implemented correctly with appropriate use of
skip_special_tokens=True.
| "id": "WlfJBFucY9gi" | ||
| }, | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.ipynb)\n" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Update Colab badge URL to point to main repository.
The Colab badge currently points to a user's fork (DhivyaBharathy-web/PraisonAI) instead of the main repository. This could lead to broken links if the fork becomes unavailable.
- "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.ipynb)\n"
+ "[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.ipynb)\n"📝 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.
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.ipynb)\n" | |
| "[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.ipynb)\n" |
🤖 Prompt for AI Agents
In examples/cookbooks/Qwen2_5_InstructionAgent.ipynb at line 30, update the
Colab badge URL to point to the main repository instead of the user's fork.
Replace the current URL segment "DhivyaBharathy-web/PraisonAI" with the main
repository's correct path to ensure the badge links correctly and remains valid.
| "outputs": [], | ||
| "source": [ | ||
| "from huggingface_hub import login\n", | ||
| "login(token=\"Enter your huggingface token\")\n" |
There was a problem hiding this comment.
Replace hardcoded token with secure authentication method.
The hardcoded placeholder token will cause authentication failures and represents a security anti-pattern. Consider implementing one of these approaches:
Option 1: Use environment variable (recommended)
-login(token="Enter your huggingface token")
+import os
+login(token=os.getenv("HF_TOKEN"))Option 2: Use getpass for secure input
-login(token="Enter your huggingface token")
+import getpass
+token = getpass.getpass("Enter your Hugging Face token: ")
+login(token=token)Option 3: Make authentication optional for public models
-login(token="Enter your huggingface token")
+# Optional: Login with your Hugging Face token for better rate limits
+# Uncomment and add your token below:
+# login(token="your_token_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.
| "login(token=\"Enter your huggingface token\")\n" | |
| import os | |
| login(token=os.getenv("HF_TOKEN")) |
🤖 Prompt for AI Agents
In examples/cookbooks/Qwen2_5_InstructionAgent.ipynb at line 124, replace the
hardcoded token string in the login function with a secure authentication
method. Use an environment variable to retrieve the token securely, or
alternatively use getpass to prompt the user for the token at runtime.
Optionally, allow authentication to be skipped for public models. This will
prevent authentication failures and improve security by avoiding hardcoded
sensitive information.
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #606 +/- ##
=======================================
Coverage 16.43% 16.43%
=======================================
Files 24 24
Lines 2160 2160
Branches 302 302
=======================================
Hits 355 355
Misses 1789 1789
Partials 16 16
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2 participants
User description
Added a new Jupyter notebook demonstrating the Qwen2.5 Instruction Agent integration.
Includes sections for dependencies, tools, YAML prompt configuration, main code, and output.
This notebook showcases loading, prompting, and generating responses using the Qwen2.5 model.
PR Type
enhancement, documentation
Description
Added a Jupyter notebook for predictive maintenance multi-agent workflow
Added a Jupyter notebook for code analysis agent
Both notebooks include detailed markdown documentation and Colab integration badges
Changes walkthrough 📝
Predictive_Maintenance_Multi_Agent_Workflow.ipynb
Add predictive maintenance multi-agent workflow notebookexamples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb
agents
scheduling
Code_Analysis_Agent.ipynb
Add code analysis agent cookbook notebookexamples/cookbooks/Code_Analysis_Agent.ipynb
PraisonAIAgents
Summary by CodeRabbit