Add Gemma2B_Instruction_Agent to Cookbooks #607
Conversation
|
Warning Rate limit exceeded@DhivyaBharathy-web has exceeded the limit for the number of commits or files that can be reviewed per hour. Please wait 3 minutes and 40 seconds before requesting another review. ⌛ How to resolve this issue?After the wait time has elapsed, a review can be triggered using the We recommend that you space out your commits to avoid hitting the rate limit. 🚦 How do rate limits work?CodeRabbit enforces hourly rate limits for each developer per organization. Our paid plans have higher rate limits than the trial, open-source and free plans. In all cases, we re-allow further reviews after a brief timeout. Please see our FAQ for further information. 📒 Files selected for processing (1)
WalkthroughFour new Jupyter notebook examples are introduced: one for AI-driven code analysis with structured reporting, one demonstrating a multi-agent predictive maintenance workflow, one showcasing chat interaction with the Qwen2.5 instruction model, and one illustrating training and inference with the Gemma 2B instruction agent. Each notebook includes environment setup, agent/task or model definitions, execution, and output display. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Notebook
participant PraisonAIAgents
participant Agent
participant Task
participant GitIngest
User->>Notebook: Provide code source (path or GitHub URL)
Notebook->>GitIngest: Ingest repository content
GitIngest-->>Notebook: Return repo summary, structure, code
Notebook->>PraisonAIAgents: Run analysis with Agent and Task
PraisonAIAgents->>Agent: Analyze code context
Agent-->>Task: Generate analysis report
Task-->>PraisonAIAgents: Return structured report
PraisonAIAgents-->>Notebook: Return CodeAnalysisReport
Notebook-->>User: Display analysis results
sequenceDiagram
participant User
participant Notebook
participant PraisonAIAgents
participant SensorMonitor
participant PerformanceAnalyzer
participant AnomalyDetector
participant FailurePredictor
participant MaintenanceScheduler
User->>Notebook: Start predictive maintenance workflow
Notebook->>PraisonAIAgents: Initiate workflow
PraisonAIAgents->>SensorMonitor: Collect sensor data
SensorMonitor-->>PraisonAIAgents: Return sensor data
PraisonAIAgents->>PerformanceAnalyzer: Analyze performance
PerformanceAnalyzer-->>PraisonAIAgents: Return analysis
PraisonAIAgents->>AnomalyDetector: Detect anomalies
AnomalyDetector-->>PraisonAIAgents: Return anomalies
PraisonAIAgents->>FailurePredictor: Predict failures
FailurePredictor-->>PraisonAIAgents: Return predictions
PraisonAIAgents->>MaintenanceScheduler: Schedule maintenance
MaintenanceScheduler-->>PraisonAIAgents: Return schedule
PraisonAIAgents-->>Notebook: Return workflow results
Notebook-->>User: Display workflow output
Possibly related PRs
Poem
Thanks for using CodeRabbit! It's free for OSS, and your support helps us grow. If you like it, consider giving us a shout-out. 🪧 TipsChatThere are 3 ways to chat with CodeRabbit:
SupportNeed help? Create a ticket on our support page for assistance with any issues or questions. Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments. CodeRabbit Commands (Invoked using PR comments)
Other keywords and placeholders
CodeRabbit Configuration File (
|
![gemini-code-assist[bot]](https://avatars.githubusercontent.com/in/956858?s=60&v=4){kind=small}
There was a problem hiding this comment.
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! gemini-code-assist here, providing a summary of this pull request. This PR, authored by DhivyaBharathy-web, adds new example notebooks to the examples/cookbooks directory. Based on the provided patch content, three distinct notebooks are being introduced: one demonstrating a Code Analysis Agent, another illustrating a multi-agent workflow for Predictive Maintenance, and a third showing a simple chat interaction with the Qwen2.5-0.5B-Instruct model using Hugging Face. It's worth noting that the PR title and description specifically mention adding a 'Gemma2B_Instruction_Agent' notebook for fine-tuning, but this particular notebook is not present in the patch content I have reviewed. The changes primarily focus on providing practical examples of using AI agents and models within a notebook environment.
Highlights
- New Cookbooks Added: Three new Jupyter notebooks are added to the
examples/cookbooksdirectory, providing diverse examples of AI agent and model usage. - Code Analysis Agent Example: A notebook demonstrating how to build an AI agent for comprehensive code analysis using
praisonaiagentsandgitingest, including defining data models for the analysis report. - Predictive Maintenance Workflow Example: A multi-agent workflow notebook showcasing a predictive maintenance pipeline, involving agents for data collection, performance analysis, anomaly detection, failure prediction, and maintenance scheduling, utilizing
praisonaiagentsandasyncio. - Qwen2.5 Chat Example: A simple notebook demonstrating how to load and use the Qwen2.5-0.5B-Instruct model from Hugging Face for basic chat generation.
- Discrepancy with Title/Description: The patch content adds notebooks for Code Analysis, Predictive Maintenance, and Qwen2.5, which differs from the PR title and description mentioning a Gemma2B Instruction Agent notebook.
Changelog
Click here to see the changelog
- examples/cookbooks/Code_Analysis_Agent.ipynb
- Added a new Jupyter notebook for a Code Analysis Agent.
- Includes installation of
praisonaiagentsandgitingest. - Defines Pydantic models (
CodeMetrics,CodeAnalysisReport) for structured output. - Sets up an
AgentandTaskfor code analysis. - Provides a function
analyze_codeto ingest code from a source and run the analysis. - Includes example code to run the analysis and display results.
- examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb
- Added a new Jupyter notebook for a Predictive Maintenance Multi-Agent Workflow.
- Includes installation of
praisonaiagents. - Defines helper functions to simulate sensor data collection, performance analysis, anomaly detection, failure prediction, and maintenance scheduling.
- Sets up multiple
AgentandTaskinstances to form a workflow. - Demonstrates running the workflow asynchronously and printing task results.
- examples/cookbooks/Qwen2_5_InstructionAgent.ipynb
- Added a new Jupyter notebook for simple chat with Qwen2.5-0.5B-Instruct.
- Includes installation of
transformersandaccelerate. - Requires Hugging Face token for authentication.
- Loads the Qwen2.5-0.5B-Instruct model and tokenizer.
- Prepares a chat prompt using the tokenizer's chat template.
- Generates a response using the loaded model.
- Prints the decoded model response.
Using Gemini Code Assist
The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.
Invoking Gemini
You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.
| Feature | Command | Description |
|---|---|---|
| Code Review | /gemini review |
Performs a code review for the current pull request in its current state. |
| Pull Request Summary | /gemini summary |
Provides a summary of the current pull request in its current state. |
| Comment | @gemini-code-assist | Responds in comments when explicitly tagged, both in issue comments and review comments. |
| Help | /gemini help |
Displays a list of available commands. |
Customization
To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.
Limitations & Feedback
Gemini Code Assist is currently in preview and may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments to provide feedback.
You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.
New notebooks arrive,
Agents and models thrive,
Code analysis,
Predictive prognosis,
In Colab, they come alive.
Footnotes
-
Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩
PR Reviewer Guide 🔍Here are some key observations to aid the review process:
|
PR Code Suggestions ✨Explore these optional code suggestions:
|
||||||||||||||
There was a problem hiding this comment.
Code Review
This pull request introduces several new example notebooks for AI agent workflows and LLM chat, which are valuable additions for users. The notebooks are generally well-structured and provide good starting points. My review focuses on enhancing the clarity of how outputs are presented in some notebooks to avoid potential confusion for users. Overall, great work on expanding the examples!
Summary of Findings
- Clarity of Output Generation: In
Code_Analysis_Agent.ipynbandPredictive_Maintenance_Multi_Agent_Workflow.ipynb, the output cells display hardcoded/mocked data. It would be beneficial to either make these cells dynamically generate output by calling the relevant functions defined earlier in the notebook or to clearly label the existing output as static examples. This will prevent users from being confused about whether the output is live or pre-canned. - API Key Handling in Examples: The notebooks use placeholders like
os.environ['OPENAI_API_KEY'] = 'your_api_key_here'. While standard for examples, it's always good practice to remind users in accompanying documentation or comments about secure API key management (e.g., using environment variables set outside the notebook, or tools likepython-dotenv). (Severity: low, not commented inline due to settings) - Error Handling in Example Code: The example functions (e.g.,
analyze_code,mainfor workflow) generally lack explicit error handling (try-except blocks). For production code, robust error handling would be crucial. In examples, this might be omitted for brevity, but it's a point to be aware of. (Severity: low, not commented inline due to settings) - Colab-specific Commands: The
Code_Analysis_Agent.ipynbincludes%cd PraisonAI. If this is essential for the notebook to run (e.g., for relative paths used bygitingest), it should be explained, especially for users running the notebook outside of a Colab environment where the directory structure might differ. (Severity: low, not commented inline due to settings) - Markdown Section Title Accuracy: In
Qwen2_5_InstructionAgent.ipynb, the section titled "⚙️ YAML Prompt (Token Authentication)" doesn't involve YAML. A title like "Hugging Face Token Authentication" would be more accurate. (Severity: low, not commented inline due to settings)
Merge Readiness
The pull request adds valuable example notebooks. However, there are a couple of medium-severity issues related to the clarity of output generation in Code_Analysis_Agent.ipynb and Predictive_Maintenance_Multi_Agent_Workflow.ipynb. Addressing these would significantly improve the user experience by making it clear whether the displayed outputs are dynamically generated or static examples. I recommend making these changes before merging. I am unable to approve the pull request myself; please ensure it is reviewed and approved by others before merging.
| "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", |
There was a problem hiding this comment.
The analysis_result in this cell is currently hardcoded. While this is useful for a static demonstration, it might be confusing since the analyze_code function is defined in a previous cell, implying that this output could be dynamically generated.
To improve clarity, could you consider one of the following?
- Modify this cell to actually call
analyze_code(example_code_source)and display its output. You'd need to defineexample_code_source(e.g., a public GitHub repo URL or instructions for a local path). - Clearly label the current hardcoded
analysis_resultas example data, perhaps with a comment in the code or a markdown note.
This would help users understand whether they are seeing a live execution result or a pre-canned example.
# To make this cell dynamically generate the report, you would call the analyze_code function.
# For example:
# code_source_to_analyze = "YOUR_GITHUB_REPO_URL_OR_LOCAL_PATH" # Replace with a real URL or path
# try:
# analysis_result = analyze_code(code_source_to_analyze)
# # If analyze_code returns a Pydantic model, convert to dict for json.dumps:
# if hasattr(analysis_result, 'model_dump'):
# analysis_result = analysis_result.model_dump()
# except Exception as e:
# print(f"Note: Live analysis failed or was skipped. Displaying example data. Error: {e}")
# # Fallback to example data if live analysis fails
# analysis_result = { ... example data ... }
# The following is pre-defined example data for demonstration purposes:
analysis_result = {
"overall_quality": 85,
"code_metrics": [
{
"category": "Architecture and Design",
"score": 80,
"findings": [
"Modular structure with clear separation of concerns.",
"Use of type annotations improves code readability and maintainability."
]
},
{
"category": "Code Maintainability",
"score": 85,
"findings": [
"Consistent use of type hints and NamedTuple for structured data.",
"Logical organization of functions and classes."
]
},
{
"category": "Performance Optimization",
"score": 75,
"findings": [
"Potential performance overhead due to repeated sys.stdout.write calls.",
"Efficient use of optional parameters to control execution flow."
]
},
{
"category": "Security Practices",
"score": 80,
"findings": [
"No obvious security vulnerabilities in the code.",
"Proper encapsulation of functionality."
]
},
{
"category": "Test Coverage",
"score": 70,
"findings": [
"Lack of explicit test cases in the provided code.",
"Use of type checking suggests some level of validation."
]
}
],
"architecture_score": 80,
"maintainability_score": 85,
"performance_score": 75,
"security_score": 80,
"test_coverage": 70,
"key_strengths": [
"Strong use of type annotations and typing extensions.",
"Clear separation of CLI argument parsing and business logic."
],
"improvement_areas": [
"Increase test coverage to ensure robustness.",
"Optimize I/O operations to improve performance."
],
"tech_stack": ["Python", "argparse", "typing_extensions"],
"recommendations": [
"Add unit tests to improve reliability.",
"Consider async I/O for improved performance in CLI tools."
],
"complexity_metrics": {"cyclomatic_complexity_example": 5}, # Example value
"best_practices": [{"name": "Code Linting", "status": "Not Evident"}], # Example value
"potential_risks": ["Limited test coverage may hide bugs."], # Example value
"documentation_quality": 60 # Example value
}
| "print(\"\"\"\n", | ||
| "[Starting Predictive Maintenance Workflow...\n", | ||
| "==================================================\n", | ||
| "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n", | ||
| "│ │\n", | ||
| "│ 👤 Agent: Sensor Monitor │\n", | ||
| "│ Role: Data Collection │\n", | ||
| "│ Tools: collect_sensor_data │\n", | ||
| "│ │\n", | ||
| "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n", | ||
| "\n", | ||
| "╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮\n", | ||
| "│ │\n", | ||
| "│ 👤 Agent: Performance Analyzer │\n", | ||
| "│ Role: Performance Analysis │\n", | ||
| "│ Tools: analyze_performance │\n", | ||
| "│ │\n", | ||
| "╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n", | ||
| "\n", | ||
| "[20:01:26] INFO [20:01:26] process.py:429 INFO Task schedule_maintenance has no next tasks, ending workflow process.py:429\n", | ||
| "\n", | ||
| "Maintenance Planning Results:\n", | ||
| "==================================================\n", | ||
| "\n", | ||
| "Task: 0\n", | ||
| "Result: The sensor readings you have collected are as follows:\n", | ||
| "\n", | ||
| "- **Temperature**: 86°F\n", | ||
| "- **Vibration**: 0.6 (units not specified, but typically measured in g-forces or mm/s)\n", | ||
| "- **Pressure**: 101 (units not specified, but typically measured in kPa or psi)\n", | ||
| "- **Noise Level**: 81 dB\n", | ||
| "\n", | ||
| "Here's a brief analysis of these readings:\n", | ||
| "\n", | ||
| "1. **Temperature**: At 86°F, the temperature is relatively warm. Depending on the context (e.g., industrial equipment, environmental monitoring), this could be within normal operating conditions or might require cooling measures if it's above the optimal range.\n", | ||
| "\n", | ||
| "2. **Vibration**: A vibration level of 0.6 is generally low, but the significance depends on the type of equipment being monitored. For precision machinery, even small vibrations can be critical, whereas for more robust equipment, this might be negligible.\n", | ||
| "\n", | ||
| "3. **Pressure**: A pressure reading of 101 is often within normal ranges for many systems, but without specific units or context, it's hard to determine if this is optimal or requires adjustment.\n", | ||
| "\n", | ||
| "4. **Noise Level**: At 81 dB, the noise level is relatively high. Prolonged exposure to noise levels above 85 dB can be harmful to hearing, so if this is a workplace environment, it might be necessary to implement noise reduction measures or provide hearing protection.\n", | ||
| "\n", | ||
| "Overall, these readings should be compared against the specific operational thresholds and safety standards relevant to the equipment or environment being monitored. If any values are outside of acceptable ranges, further investigation or corrective actions may be needed.\n", | ||
| "--------------------------------------------------\n", | ||
| "\n", | ||
| "Task: 1\n", | ||
| "Result: Based on the provided operational metrics, here's an analysis of the equipment performance:\n", | ||
| "\n", | ||
| "1. **Efficiency (94%)**:\n", | ||
| " - The equipment is operating at a high efficiency level, with 94% of the input being effectively converted into useful output. This suggests\n", | ||
| "that the equipment is well-maintained and optimized for performance. However, there is still a 6% margin for improvement, which could be addressed by identifying and minimizing any inefficiencies in the process.\n", | ||
| "\n", | ||
| "2. **Uptime (99%)**:\n", | ||
| " - The equipment has an excellent uptime rate of 99%, indicating that it is available and operational almost all the time. This is a strong indicator of reliability and suggests that downtime due to maintenance or unexpected failures is minimal. Maintaining this level of uptime should\n", | ||
| "be a priority, as it directly impacts productivity and operational continuity.\n", | ||
| "\n", | ||
| "3. **Output Quality (94%)**:\n", | ||
| " - The output quality is also at 94%, which is a positive sign that the equipment is producing high-quality products or results. However, similar to efficiency, there is room for improvement. Efforts could be made to identify any factors that might be affecting quality, such as calibration issues, material inconsistencies, or process deviations.\n", | ||
| "\n", | ||
| "**Overall Assessment**:\n", | ||
| "The equipment is performing well across all key metrics, with high efficiency, uptime, and output quality. To further enhance performance, focus should be placed on fine-tuning processes to close the small gaps in efficiency and quality. Regular maintenance, monitoring, and process optimization can help sustain and potentially improve these metrics.\n", | ||
| "--------------------------------------------------]\n", | ||
| "\"\"\")" |
There was a problem hiding this comment.
This cell prints a large, hardcoded string representing the output of the predictive maintenance workflow. While useful for showing what the output looks like, it's not dynamically generated by the await main() call in the preceding cell.
To avoid misleading users, could you clarify that this is an example output? You could:
- Add a markdown cell before this one explaining that the following is a sample output.
- Modify the
printstatement to include a note, e.g.,print("""\n--- Example Output --- ... """).
Alternatively, if feasible for an example, you could modify the main() function to return its results, and then this cell could print those dynamic results, perhaps with a note that actual LLM outputs can vary.
# The following is a pre-defined example of what the output from the workflow might look like.
# To see live output, you would typically capture and print the 'results' from the `await main()` call in the previous cell.
print("""
[Starting Predictive Maintenance Workflow...
==================================================
╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ │
│ 👤 Agent: Sensor Monitor │
│ Role: Data Collection │
│ Tools: collect_sensor_data │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Agent Info ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ │
│ 👤 Agent: Performance Analyzer │
│ Role: Performance Analysis │
│ Tools: analyze_performance │
│ │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
[20:01:26] INFO [20:01:26] process.py:429 INFO Task schedule_maintenance has no next tasks, ending workflow process.py:429
Maintenance Planning Results:
==================================================
Task: 0
Result: The sensor readings you have collected are as follows:
- **Temperature**: 86°F
- **Vibration**: 0.6 (units not specified, but typically measured in g-forces or mm/s)
- **Pressure**: 101 (units not specified, but typically measured in kPa or psi)
- **Noise Level**: 81 dB
Here's a brief analysis of these readings:
1. **Temperature**: At 86°F, the temperature is relatively warm. Depending on the context (e.g., industrial equipment, environmental monitoring), this could be within normal operating conditions or might require cooling measures if it's above the optimal range.
2. **Vibration**: A vibration level of 0.6 is generally low, but the significance depends on the type of equipment being monitored. For precision machinery, even small vibrations can be critical, whereas for more robust equipment, this might be negligible.
3. **Pressure**: A pressure reading of 101 is often within normal ranges for many systems, but without specific units or context, it's hard to determine if this is optimal or requires adjustment.
4. **Noise Level**: At 81 dB, the noise level is relatively high. Prolonged exposure to noise levels above 85 dB can be harmful to hearing, so if this is a workplace environment, it might be necessary to implement noise reduction measures or provide hearing protection.
Overall, these readings should be compared against the specific operational thresholds and safety standards relevant to the equipment or environment being monitored. If any values are outside of acceptable ranges, further investigation or corrective actions may be needed.
--------------------------------------------------
Task: 1
Result: Based on the provided operational metrics, here's an analysis of the equipment performance:
1. **Efficiency (94%)**:
- The equipment is operating at a high efficiency level, with 94% of the input being effectively converted into useful output. This suggests
that the equipment is well-maintained and optimized for performance. However, there is still a 6% margin for improvement, which could be addressed by identifying and minimizing any inefficiencies in the process.
2. **Uptime (99%)**:
- The equipment has an excellent uptime rate of 99%, indicating that it is available and operational almost all the time. This is a strong indicator of reliability and suggests that downtime due to maintenance or unexpected failures is minimal. Maintaining this level of uptime should
be a priority, as it directly impacts productivity and operational continuity.
3. **Output Quality (94%)**:
- The output quality is also at 94%, which is a positive sign that the equipment is producing high-quality products or results. However, similar to efficiency, there is room for improvement. Efforts could be made to identify any factors that might be affecting quality, such as calibration issues, material inconsistencies, or process deviations.
**Overall Assessment**:
The equipment is performing well across all key metrics, with high efficiency, uptime, and output quality. To further enhance performance, focus should be placed on fine-tuning processes to close the small gaps in efficiency and quality. Regular maintenance, monitoring, and process optimization can help sustain and potentially improve these metrics.
--------------------------------------------------]
""")
![coderabbitai[bot]](https://avatars.githubusercontent.com/in/347564?s=60&v=4){kind=small}
There was a problem hiding this comment.
Actionable comments posted: 5
♻️ Duplicate comments (2)
examples/cookbooks/Code_Analysis_Agent.ipynb (2)
17-17: Fix Colab badge URL to point to main repository.The URL points to a user fork instead of the main repository, similar to the other notebooks.
-[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Code_Analysis_Agent.ipynb) +[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Code_Analysis_Agent.ipynb)
67-67: Remove hardcoded API key placeholder.Same security issue as the other notebooks - hardcoded placeholder could cause authentication failures.
-os.environ['OPENAI_API_KEY'] = 'your_api_key_here' +# os.environ['OPENAI_API_KEY'] = 'your_api_key_here' # Uncomment and add your key
🧹 Nitpick comments (1)
examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb (1)
238-301: Consider replacing hardcoded output with dynamic execution.The example output is embedded as a static string rather than showing actual workflow execution results. This approach may confuse users about what the actual output format looks like.
Consider either:
- Showing actual execution results
- Clearly labeling this as "Expected Output Example"
- Moving this to a markdown cell with proper formatting
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (3)
examples/cookbooks/Code_Analysis_Agent.ipynb(1 hunks)examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb(1 hunks)examples/cookbooks/Qwen2_5_InstructionAgent.ipynb(1 hunks)
🔇 Additional comments (5)
examples/cookbooks/Qwen2_5_InstructionAgent.ipynb (1)
342-359: LGTM! Well-structured model loading and inference.The model loading implementation follows best practices with automatic device mapping and dtype selection. The chat template usage and token generation are correctly implemented.
examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb (1)
114-159: Well-designed mock functions for demonstration.The helper functions provide realistic simulation of sensor data collection and maintenance workflow processes. The time-based variation adds believable randomness to the mock data.
examples/cookbooks/Code_Analysis_Agent.ipynb (3)
191-222: Well-structured code analysis function.The function properly uses gitingest for code ingestion and formats the context appropriately for analysis. The structured approach with summary, tree, and content sections is logical.
347-409: Example output is missing required model fields.The hardcoded example output doesn't include
complexity_metrics,best_practices,potential_risks, anddocumentation_qualityfields that are defined in the Pydantic model.The example should match the model structure or indicate it's a partial example.
Likely an incorrect or invalid review comment.
95-115:⚠️ Potential issueCodeAnalysisReport model is missing fields referenced in example output.
The Pydantic model doesn't include
complexity_metrics,best_practices,potential_risks, anddocumentation_qualityfields that are shown in the example output, which could cause validation errors.The example output includes fields not defined in the model. Either remove these from the example or add them to the model:
class CodeAnalysisReport(BaseModel): overall_quality: int code_metrics: List[CodeMetrics] architecture_score: int maintainability_score: int performance_score: int security_score: int test_coverage: int key_strengths: List[str] improvement_areas: List[str] tech_stack: List[str] recommendations: List[str] + complexity_metrics: Dict[str, int] + best_practices: List[Dict[str, str]] + potential_risks: List[str] + documentation_quality: intLikely an incorrect or invalid review comment.
| "outputs": [], | ||
| "source": [ | ||
| "from huggingface_hub import login\n", | ||
| "login(token=\"Enter your huggingface token\")\n" |
There was a problem hiding this comment.
Remove hardcoded authentication token placeholder.
The login function contains a placeholder string that could mislead users. This should use a more secure approach or clear instructions.
-login(token="Enter your huggingface token")
+# login(token="your_huggingface_token_here") # Uncomment and add your token
+# Or use: huggingface_hub.login() for interactive login📝 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" | |
| # login(token="your_huggingface_token_here") # Uncomment and add your token | |
| # Or use: huggingface_hub.login() for interactive login |
🤖 Prompt for AI Agents
In examples/cookbooks/Qwen2_5_InstructionAgent.ipynb at line 124, the login
function uses a hardcoded placeholder token string which can mislead users and
is insecure. Replace the hardcoded token string with a prompt or environment
variable retrieval method to securely obtain the token, and update the code
comments to provide clear instructions on how users should supply their
authentication token.
| "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
Fix Colab badge URL to point to main repository.
The Colab badge URL points to a user fork (DhivyaBharathy-web/PraisonAI) instead of the main repository. This should reference the canonical repository URL.
-[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.ipynb)
+[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Qwen2_5_InstructionAgent.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/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, the Colab badge
URL incorrectly points to the user fork 'DhivyaBharathy-web/PraisonAI'. Update
the URL to reference the main repository's canonical URL to ensure it directs
users to the correct Colab notebook.
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = 'enter your api key'" |
There was a problem hiding this comment.
Remove hardcoded API key placeholder.
The environment variable is set with a placeholder string that could lead to authentication errors. Use a more secure approach.
-os.environ['OPENAI_API_KEY'] = 'enter your api key'
+# os.environ['OPENAI_API_KEY'] = 'your_openai_api_key_here' # Uncomment and add your key
+# Or use: os.environ['OPENAI_API_KEY'] = getpass.getpass("Enter 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['OPENAI_API_KEY'] = 'enter your api key'" | |
| # os.environ['OPENAI_API_KEY'] = 'your_openai_api_key_here' # Uncomment and add your key | |
| # Or use: os.environ['OPENAI_API_KEY'] = getpass.getpass("Enter OpenAI API Key: ") |
🤖 Prompt for AI Agents
In examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb at line
66, remove the hardcoded placeholder string for the OPENAI_API_KEY environment
variable. Instead, instruct users to set the API key securely outside the code,
such as through their system environment variables or a secure secrets manager,
to avoid authentication errors and improve security.
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Predictive_Maintenance_Multi-Agent_Workflow.ipynb)" |
There was a problem hiding this comment.
Fix filename mismatch in Colab badge URL.
The URL uses underscores in the filename while the actual file uses hyphens. This will result in a 404 error when users click the badge.
-[](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/MervinPraison/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/MervinPraison/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 uses underscores in the filename instead of hyphens,
causing a 404 error. Update the URL to replace underscores with hyphens to match
the actual filename so the badge correctly links to the notebook.
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "%cd PraisonAI" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Remove disconnected directory change command.
The %cd PraisonAI command appears unrelated to the code analysis functionality and may confuse users about the notebook's purpose.
-%cd PraisonAIConsider removing this cell or explaining its purpose if it's needed for the demonstration.
📝 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.
| "%cd PraisonAI" |
🤖 Prompt for AI Agents
In examples/cookbooks/Code_Analysis_Agent.ipynb at line 421, remove the line
containing the '%cd PraisonAI' command because it is unrelated to the code
analysis functionality and may confuse users. If the directory change is
necessary for the demonstration, replace it with a clear explanation of why it
is needed; otherwise, simply delete this command to keep the notebook focused
and clear.
There was a problem hiding this comment.
Actionable comments posted: 4
🧹 Nitpick comments (1)
examples/cookbooks/Gemma2B_Instruction_Agent.ipynb (1)
523-531: Inference implementation follows best practices.Using
apply_chat_templateis the correct approach for chat-based models. The code properly handles tokenization and generation.Consider adding a note about the truncated output (ending at "3") being due to the
max_new_tokens=100limit.
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
📒 Files selected for processing (1)
examples/cookbooks/Gemma2B_Instruction_Agent.ipynb(1 hunks)
🔇 Additional comments (4)
examples/cookbooks/Gemma2B_Instruction_Agent.ipynb (4)
46-46: Dependencies look appropriate for the use case.The package selection (transformers, accelerate, datasets, bitsandbytes) is well-suited for working with large language models and the quiet installation flag keeps notebook output clean.
358-365: Model loading configuration follows best practices.The use of
device_map="auto"andtorch.float16provides good performance optimizations for inference.
463-478: Dataset creation and tokenization implemented correctly.The sample dataset provides good educational content and the tokenization parameters (padding, truncation, max_length=64) are appropriate for the demo use case.
573-574: Model saving implemented correctly.The model and tokenizer are properly saved to the local directory for future use.
| "# Output\n", | ||
| "`Photosynthesis is how plants eat sunlight! 🌞 They use air, water, and sunlight to make food and grow.`" | ||
| ] |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Update output example to match actual inference results.
The hardcoded output example doesn't match the actual model response shown in the inference section, which could mislead users about expected behavior.
Consider replacing with the actual output or noting that this is just an example of typical responses.
🤖 Prompt for AI Agents
In examples/cookbooks/Gemma2B_Instruction_Agent.ipynb around lines 584 to 586,
the hardcoded output example does not match the actual inference results shown
earlier, which may confuse users. Update the output example to reflect the
actual model response from the inference section or add a note clarifying that
the output is a typical example and may vary.
| "source": [ | ||
| "# 🌱 Gemma 2B Instruction Agent\n", | ||
| "\n", | ||
| "**Goal:** You will learn how to do data prep, how to train, how to run the model, and how to save it using Google’s `gemma-2b-it` open-source model.\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Clarify notebook scope to match actual content.
The description promises to show "how to train" the model, but the notebook only demonstrates inference and model saving. No actual training code is present.
Consider updating the description to accurately reflect the content:
-**Goal:** You will learn how to do data prep, how to train, how to run the model, and how to save it using Google's `gemma-2b-it` open-source model.
+**Goal:** You will learn how to load, run inference, and save Google's `gemma-2b-it` open-source model with sample data preparation.Or add actual training code to fulfill the promise.
📝 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.
| "**Goal:** You will learn how to do data prep, how to train, how to run the model, and how to save it using Google’s `gemma-2b-it` open-source model.\n", | |
| "**Goal:** You will learn how to load, run inference, and save Google's `gemma-2b-it` open-source model with sample data preparation.\n", |
🤖 Prompt for AI Agents
In examples/cookbooks/Gemma2B_Instruction_Agent.ipynb at line 12, the notebook
description claims it will show how to train the model, but no training code is
present. To fix this, either update the description to remove or rephrase the
training promise to match the demonstrated inference and saving steps, or add
the missing training code to fulfill the original scope.
| } | ||
| ], | ||
| "source": [ | ||
| "prompt = \"You are Qwen, a helpful assistant.\\nUser: What is the capital of France?\\nAssistant:\"\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Fix model name inconsistency in prompt.
The prompt references "Qwen" but this notebook is about the Gemma model, which creates confusion.
-prompt = "You are Qwen, a helpful assistant.\\nUser: What is the capital of France?\\nAssistant:"
+prompt = "You are Gemma, a helpful assistant.\\nUser: What is the capital of France?\\nAssistant:"📝 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.
| "prompt = \"You are Qwen, a helpful assistant.\\nUser: What is the capital of France?\\nAssistant:\"\n", | |
| prompt = "You are Gemma, a helpful assistant.\nUser: What is the capital of France?\nAssistant:" |
🤖 Prompt for AI Agents
In examples/cookbooks/Gemma2B_Instruction_Agent.ipynb at line 402, the prompt
string incorrectly references "Qwen" as the assistant's name, causing
inconsistency with the Gemma model context. Update the prompt to replace "Qwen"
with "Gemma" to accurately reflect the model being used and avoid confusion.
| "from datasets import load_dataset\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "login(\"Enter your token here\")\n", |
There was a problem hiding this comment.
Replace hardcoded token placeholder with secure authentication.
The hardcoded placeholder "Enter your token here" is problematic as users might accidentally commit their actual tokens or be confused about proper authentication.
Consider these secure alternatives:
-login("Enter your token here")
+# Option 1: Use environment variable
+login(token=os.getenv("HF_TOKEN"))
+
+# Option 2: Prompt user securely
+from getpass import getpass
+login(token=getpass("Enter your HuggingFace token: "))
+
+# Option 3: Use HF CLI login (recommended)
+# Run: huggingface-cli login
+login()Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In examples/cookbooks/Gemma2B_Instruction_Agent.ipynb at line 356, replace the
hardcoded token placeholder string "Enter your token here" with a secure
authentication method. Instead of embedding tokens directly in the code, prompt
the user to input their token at runtime or load it securely from environment
variables or a protected configuration file. This prevents accidental token
exposure and improves security.
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #607 +/- ##
=======================================
Coverage 16.43% 16.43%
=======================================
Files 24 24
Lines 2160 2160
Branches 302 302
=======================================
Hits 355 355
Misses 1789 1789
Partials 16 16
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
|
2 participants
User description
This notebook sets up a fine-tuning pipeline for the google/gemma-2-2b-it model using a small in-memory dataset.
It includes tokenization, training with transformers.Trainer, inference, and model saving.
The setup is lightweight and avoids external dataset loading issues for quick testing and experimentation.
PR Type
Documentation, Enhancement
Description
Add new example notebooks for AI agent workflows and LLM chat.
Each notebook includes step-by-step code, markdown explanations, and sample outputs.
Demonstrates practical usage of PraisonAIAgents and LLMs for real-world tasks.
Changes walkthrough 📝
Code_Analysis_Agent.ipynb
Add code analysis agent example notebookexamples/cookbooks/Code_Analysis_Agent.ipynb
Predictive_Maintenance_Multi_Agent_Workflow.ipynb
Add predictive maintenance multi-agent workflow notebookexamples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb
Qwen2_5_InstructionAgent.ipynb
Add Qwen2.5 Instruction Agent chat demo notebookexamples/cookbooks/Qwen2_5_InstructionAgent.ipynb
display.
Summary by CodeRabbit