Add tinyllama model agent #608
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WalkthroughOne notebook's "Open in Colab" badge URL is corrected to match the filename. Four new Jupyter notebooks are added demonstrating various AI agents and workflows: Gemma 2B instruction agent, predictive maintenance multi-agent workflow, Qwen2.5 instruction agent, and TinyLlama simple AI agent. These cover setup, model loading, inference, multi-agent orchestration, and saving models. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Notebook
participant AI_Agent
participant Model/Workflow
User->>Notebook: Run notebook cells
Notebook->>Model/Workflow: Setup (install, import, authenticate)
Notebook->>AI_Agent: Define/configure agent(s) and tasks
Notebook->>Model/Workflow: Provide input (code, prompt, data)
Model/Workflow->>AI_Agent: Analyze/process/generate output
AI_Agent->>Notebook: Return results
Notebook->>User: Display structured output/results
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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. The title and description mention adding a TinyLlama model agent, demonstrating a minimal setup with Hugging Face Transformers. However, the provided patch content introduces three new example notebooks showcasing different agents and workflows: a Code Analysis Agent, a Gemma 2B Instruction Agent, and a Predictive Maintenance Multi-Agent Workflow. This summary will focus on the changes present in the provided patches.
This PR adds these three distinct examples to the examples/cookbooks directory. The notebooks demonstrate how to set up and use different types of AI agents and multi-agent workflows, leveraging libraries like praisonaiagents, gitingest, and Hugging Face Transformers with specific models (Gemma 2B, Qwen 2.5).
Highlights
- New Code Analysis Agent Example: Adds a Jupyter notebook (
examples/cookbooks/Code_Analysis_Agent.ipynb) demonstrating an AI agent for comprehensive code analysis and quality assessment. It defines Pydantic models for structuring the analysis report and usesgitingestto process code content. - New Gemma 2B Instruction Agent Example: Adds a Jupyter notebook (
examples/cookbooks/Gemma2B_Instruction_Agent.ipynb) showing how to use thegoogle/gemma-2-2b-itmodel via Hugging Face Transformers for instruction-based generation. It includes steps for dependency installation, model loading, and inference. - New Predictive Maintenance Workflow Example: Adds a Jupyter notebook (
examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb) illustrating a multi-agent workflow for predictive maintenance. It defines several helper functions simulating data collection, analysis, anomaly detection, failure prediction, and maintenance scheduling, orchestrated bypraisonaiagents. - New Qwen 2.5 Instruction Agent Example: Adds a Jupyter notebook (
examples/cookbooks/Qwen2_5_InstructionAgent.ipynb) demonstrating simple chat generation using theQwen/Qwen2.5-0.5B-Instructmodel with Hugging Face Transformers. It covers setup, authentication, and generating responses from a chat prompt.
Changelog
Click here to see the changelog
- examples/cookbooks/Code_Analysis_Agent.ipynb
- Added new notebook for Code Analysis Agent.
- Includes setup for dependencies (
praisonaiagents,gitingest). - Defines
CodeMetricsandCodeAnalysisReportPydantic models. - Configures a
Code Analysis Expertagent and task. - Provides
analyze_codefunction usinggitingestandPraisonAIAgents. - Includes example execution and output.
- examples/cookbooks/Gemma2B_Instruction_Agent.ipynb
- Added new notebook for Gemma 2B Instruction Agent.
- Includes setup for dependencies (
transformers,accelerate,datasets,bitsandbytes). - Demonstrates Hugging Face Hub login.
- Loads
google/gemma-2-2b-ittokenizer and model. - Shows example prompt configuration and generation.
- Includes example of tokenizing a sample dataset.
- Demonstrates inference with chat template.
- Shows how to save the fine-tuned model and tokenizer.
- examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb
- Added new notebook for Predictive Maintenance Multi-Agent Workflow.
- Includes setup for
praisonaiagentsdependency. - Defines helper functions for simulating sensor data, performance analysis, anomaly detection, failure prediction, and maintenance scheduling.
- Defines multiple agents (
Sensor Monitor,Performance Analyzer,Anomaly Detector,Failure Predictor,Maintenance Scheduler). - Defines tasks with dependencies to form a workflow.
- Demonstrates running the workflow asynchronously.
- Includes example output of the workflow results.
- examples/cookbooks/Qwen2_5_InstructionAgent.ipynb
- Added new notebook for Qwen 2.5 Instruction Agent.
- Includes setup for
transformersandacceleratedependencies. - Demonstrates Hugging Face Hub login.
- Loads
Qwen/Qwen2.5-0.5B-Instructtokenizer and model. - Sets up a chat message list.
- Applies chat template and generates response.
- Prints the generated response.
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Four notebooks appear,
Agents, models, crystal clear.
Code, Gemma, Qwen,
Maintenance again,
Examples banish all fear.
Footnotes
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Code Review
This pull request adds several new Jupyter notebook examples for different AI agent use cases. The examples cover code analysis, instruction-following with Gemma and Qwen models, and a predictive maintenance workflow. While these additions are valuable for showcasing the capabilities of praisonaiagents, there are a few areas for improvement, particularly concerning the accuracy of descriptions, placeholder credentials, and the use of hardcoded outputs in examples.
A significant point is that the PR title "Add tinyllama model agent" does not accurately reflect the content of the added notebooks, as they feature Gemma, Qwen, or are model-agnostic, rather than focusing on TinyLlama. This should be clarified or updated.
Summary of Findings
- Misleading PR Title: The PR title 'Add tinyllama model agent' does not accurately reflect the content of the added notebooks, which use Gemma, Qwen, or are model-agnostic, rather than focusing on TinyLlama.
- Placeholder Credentials: Multiple notebooks use placeholder API keys or tokens (e.g., 'your_api_key_here', 'Enter your token here'). While good for preventing accidental exposure, they should be accompanied by clear instructions on secure credential management.
- Hardcoded Outputs in Examples: Several notebooks (
Code_Analysis_Agent.ipynb,Predictive_Maintenance_Multi_Agent_Workflow.ipynb) contain cells with hardcoded outputs. For 'cookbook' style examples, it's crucial that outputs are generated by executing the code within the notebook to ensure verifiability and ease of adaptation for users. - Inconsistent Model Information: In
Gemma2B_Instruction_Agent.ipynb, there's a potential typo in the model ID (google/gemma-2-2b-itvs.google/gemma-2b-it), and the system prompt refers to 'Qwen' instead of 'Gemma'. - Misleading Fine-tuning Implication: The
Gemma2B_Instruction_Agent.ipynbimplies it covers training/fine-tuning in its goal description and saved model name, but no such steps are present in the notebook. - Clarity of Workflow Conditions: In
Predictive_Maintenance_Multi_Agent_Workflow.ipynb, the meaning of an empty string as a condition target in a decision task could be clarified.
Merge Readiness
This pull request introduces valuable examples, but there are several high and medium severity issues that should be addressed before merging. Key concerns include the misleading PR title, the use of hardcoded outputs instead of live execution in example notebooks, and inconsistencies in model information and stated goals within the notebooks. Addressing these points will significantly improve the clarity, accuracy, and usability of these examples. I am unable to approve the pull request in its current state; please ensure these changes are 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", |
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The output in this cell is generated from a hardcoded analysis_result dictionary. For a 'cookbook' example, it's more instructive and verifiable if the output is dynamically generated by executing the analyze_code function defined earlier in the notebook. This would allow users to see the agent in action and adapt the example more easily.
Could this cell be updated to call analyze_code (perhaps with a small, publicly accessible GitHub repository or a sample local codebase) and display its live results?
# Example: Replace the hardcoded analysis_result with a live call
# github_url = "https://github.com/user/small-sample-repo" # Replace with a real, small repo URL
# report = analyze_code(github_url)
#
# # Display the report (ensure 'report' is serializable, e.g., Pydantic model to dict)
# if report:
# analysis_result_live = report.model_dump() # If 'report' is a Pydantic model
# else:
# analysis_result_live = {"error": "Analysis failed or returned no result."}
#
# # Display Agent Info and Analysis Report
# display(Markdown(agent_info))
# print("─── 📊 AGENT CODE ANALYSIS REPORT ───")
# print(json.dumps(analysis_result_live, indent=4))
#
# For now, to keep the structure, let's assume you'll replace the static dict later.
# This is a placeholder to illustrate where the live call would go.
# To make this suggestion directly applicable, you'd replace the static dict below.
# For a direct suggestion, you'd modify the lines that define `analysis_result`.
# The current suggestion is conceptual due to the size of the replacement.
# Consider replacing lines 347-409 with a call to analyze_code and its output handling.
# For example:
# code_source_example = "https://github.com/DhivyaBharathy-web/PraisonAI" # Or a path to a local directory
# analysis_result_obj = analyze_code(code_source_example)
# analysis_result = analysis_result_obj.model_dump() if analysis_result_obj else {}
| "\n", | ||
| "login(\"Enter your token here\")\n", | ||
| "\n", | ||
| "model_id = \"google/gemma-2-2b-it\"\n", |
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The model_id is set to "google/gemma-2-2b-it". The -2- part seems like a typo; common Gemma 2B model IDs are usually google/gemma-2b-it or google/gemma-2b.
Additionally, the PR title is "Add tinyllama model agent", but this notebook uses a Gemma model. This discrepancy could be confusing. Could you verify the model ID and, if TinyLlama is intended for this PR, perhaps include an example for it or update the PR's description to reflect the models actually used?
model_id = "google/gemma-2b-it"
| "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.
The goal description in the first markdown cell mentions: "You will learn how to do data prep, how to train, how to run the model, and how to save it...". However, the notebook does not include any model training or fine-tuning steps. It tokenizes a small sample dataset and later saves the pre-trained model under a name suggesting fine-tuning (gemma-finetuned-demo in cell 12). This is misleading.
Could you either add the fine-tuning steps or update the notebook's goal and the saved model's name to accurately reflect its content (e.g., focus on inference and perhaps rename the saved model to gemma-inference-demo)?
| "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", | ||
| "\"\"\")" |
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This cell (cell 14) contains hardcoded output. For a 'cookbook' demonstrating a workflow, it's more effective to show the actual output generated by running the await workflow.astart() call from the preceding cell (cell 13). This allows users to see the workflow in action and verify its behavior.
Could cell 14 be removed, and the notebook rely on the output generated by executing cell 13? If cell 14 is intended to be purely illustrative of expected output, this should be clearly stated, though live execution is generally preferred for examples.
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = 'your_api_key_here'" |
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The notebook uses a placeholder API key your_api_key_here. While common in examples, it's good practice to guide users on securely managing API keys. Could you consider adding a markdown cell or a comment explaining how to set the OPENAI_API_KEY environment variable securely, for instance, using a .env file with python-dotenv or Colab secrets for users running in that environment?
| "from datasets import load_dataset\n", | ||
| "import torch\n", | ||
| "\n", | ||
| "login(\"Enter your token here\")\n", |
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This cell uses a placeholder Hugging Face token: login("Enter your token here"). It would be beneficial to guide users on securely managing their tokens, for example, by using Colab secrets or environment variables, rather than hardcoding them.
| } | ||
| ], | ||
| "source": [ | ||
| "prompt = \"You are Qwen, a helpful assistant.\\nUser: What is the capital of France?\\nAssistant:\"\n", |
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The prompt "You are Qwen, a helpful assistant..." refers to "Qwen", but this notebook is titled "Gemma 2B Instruction Agent" and loads a Gemma model. This inconsistency could confuse users.
Would it be clearer to update the prompt to align with the Gemma model being used?
prompt = "You are Gemma, a helpful assistant.\nUser: What is the capital of France?\nAssistant:"
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "os.environ['OPENAI_API_KEY'] = 'enter your api key'" |
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This cell uses a placeholder API key: os.environ['OPENAI_API_KEY'] = 'enter your api key'. To improve security and usability, could you add guidance for users on how to set this environment variable securely, such as using Colab secrets or a .env file?
| "sensor_task = Task(\"collect_data\", \"Collect sensor data\", \"Sensor readings\", sensor_monitor, is_start=True, next_tasks=[\"analyze_performance\"], async_execution=True)\n", | ||
| "performance_task = Task(\"analyze_performance\", \"Analyze performance metrics\", \"Performance analysis\", performance_analyzer, next_tasks=[\"detect_anomalies\"], async_execution=True)\n", | ||
| "anomaly_task = Task(\"detect_anomalies\", \"Detect operational anomalies\", \"Detected anomalies\", anomaly_detector, next_tasks=[\"predict_failures\"], context=[sensor_task, performance_task])\n", | ||
| "prediction_task = Task(\"predict_failures\", \"Predict potential failures\", \"Failure predictions\", failure_predictor, next_tasks=[\"schedule_maintenance\"], task_type=\"decision\", condition={\"critical\": [\"schedule_maintenance\"], \"warning\": [\"schedule_maintenance\"], \"normal\": \"\"})\n", |
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In the prediction_task definition, the condition dictionary includes "normal": "". It's not immediately clear what an empty string signifies for the workflow's progression when the condition is 'normal'.
Could you add a comment or a note in the markdown to explain the behavior when a condition maps to an empty string? For instance, does it mean the workflow branch terminates, or is there a default next step?
| "outputs": [], | ||
| "source": [ | ||
| "from huggingface_hub import login\n", | ||
| "login(token=\"Enter your huggingface token\")\n" |
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The notebook uses a placeholder Hugging Face token: login(token="Enter your huggingface token"). It would be helpful to guide users on managing this token securely, for example, by suggesting the use of Colab secrets or environment variables.
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Actionable comments posted: 5
🧹 Nitpick comments (2)
examples/cookbooks/Gemma2B_Instruction_Agent.ipynb (1)
463-478: Good dataset creation and tokenization workflow.The sample dataset creation and tokenization function demonstrate proper data handling. However, consider adding validation for edge cases.
Consider adding input validation:
def tokenize_function(example): + if not example['text'] or not example['text'].strip(): + raise ValueError("Empty text found in dataset") return tokenizer(example['text'], padding='max_length', truncation=True, max_length=64)examples/cookbooks/Code_Analysis_Agent.ipynb (1)
421-421: Directory change command may cause issues.The
%cd PraisonAIcommand assumes a specific directory structure that may not exist for all users.Consider making this optional or adding error handling:
-%cd PraisonAI +# Optional: Change to PraisonAI directory if it exists +import os +if os.path.exists('PraisonAI'): + %cd PraisonAI +else: + print("PraisonAI directory not found - continuing in current directory")
🛑 Comments failed to post (5)
examples/cookbooks/Qwen2_5_InstructionAgent.ipynb (1)
123-124:
⚠️ Potential issueSecurity concern: Hardcoded token placeholder in authentication.
The authentication code uses a hardcoded placeholder "Enter your huggingface token" which won't work and could mislead users. Consider using a more secure approach or clear instructions.
Apply this diff to improve the authentication approach:
-from huggingface_hub import login -login(token="Enter your huggingface token") +from huggingface_hub import login +# Option 1: Use environment variable (recommended) +# login() # This will use HF_TOKEN environment variable + +# Option 2: Use Google Colab secrets (for Colab users) +# from google.colab import userdata +# login(token=userdata.get('HF_TOKEN')) + +# Option 3: Prompt user for token +# import getpass +# token = getpass.getpass("Enter your Hugging Face token: ") +# login(token=token)📝 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.from huggingface_hub import login # Option 1: Use environment variable (recommended) # login() # This will use HF_TOKEN environment variable # Option 2: Use Google Colab secrets (for Colab users) # from google.colab import userdata # login(token=userdata.get('HF_TOKEN')) # Option 3: Prompt user for token # import getpass # token = getpass.getpass("Enter your Hugging Face token: ") # login(token=token)🤖 Prompt for AI Agents
In examples/cookbooks/Qwen2_5_InstructionAgent.ipynb around lines 123 to 124, the code hardcodes a placeholder token string for Huggingface login, which is insecure and non-functional. Replace the hardcoded token with a method to securely input the token, such as prompting the user to enter it at runtime or reading it from an environment variable, and update the code comments to clearly instruct users on how to provide their token securely.examples/cookbooks/Gemma2B_Instruction_Agent.ipynb (1)
356-356:
⚠️ Potential issueSame authentication security issue as the previous notebook.
The hardcoded token placeholder creates a non-functional authentication step.
Apply the same secure authentication approach as suggested for the Qwen notebook.
🤖 Prompt for AI Agents
In examples/cookbooks/Gemma2B_Instruction_Agent.ipynb at line 356, the authentication token is hardcoded as a placeholder string, which is insecure and non-functional. Replace this hardcoded token with a secure method to input the token, such as prompting the user to enter it at runtime or retrieving it from a secure environment variable, following the same secure authentication approach used in the Qwen notebook.examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb (1)
65-66:
⚠️ Potential issueAPI key handling has the same security issue.
The hardcoded placeholder for the OpenAI API key is not secure and won't work.
Apply this diff for secure API key handling:
-import os -os.environ['OPENAI_API_KEY'] = 'enter your api key' +import os +# Recommended: Use environment variable +# os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY') + +# For Google Colab users: +# from google.colab import userdata +# os.environ['OPENAI_API_KEY'] = userdata.get('OPENAI_API_KEY') + +# For interactive input: +# import getpass +# os.environ['OPENAI_API_KEY'] = getpass.getpass("Enter your OpenAI API key: ")🤖 Prompt for AI Agents
In examples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb around lines 65 to 66, the OpenAI API key is hardcoded as a placeholder string, which is insecure and non-functional. Replace this by loading the API key securely from an environment variable or a secure vault instead of hardcoding it. Use a method like reading from os.environ or a configuration file to set the API key dynamically at runtime.examples/cookbooks/Code_Analysis_Agent.ipynb (2)
66-67:
⚠️ Potential issueSame API key security issue.
The hardcoded placeholder approach is insecure and non-functional.
Apply the same secure API key handling approach as suggested for the previous notebooks.
🤖 Prompt for AI Agents
In examples/cookbooks/Code_Analysis_Agent.ipynb around lines 66 to 67, the API key is hardcoded insecurely as a placeholder string. Replace this with a secure method to load the API key, such as reading it from environment variables or a secure configuration file, to avoid exposing sensitive information and ensure the key is properly set at runtime.
191-222: 🛠️ Refactor suggestion
Robust code analysis function implementation.
The function properly handles repository ingestion and context formatting. However, consider adding error handling for edge cases.
Add error handling to improve robustness:
def analyze_code(code_source: str) -> CodeAnalysisReport: """ Analyze code from directory path or GitHub URL """ + try: # Ingest code content summary, tree, content = ingest(code_source) + except Exception as e: + raise ValueError(f"Failed to ingest code from {code_source}: {str(e)}") + if not content or not content.strip(): + raise ValueError("No code content found to analyze") + # Concatenate context into structured format context_text = f""" CODE REPOSITORY ANALYSIS ======================= SUMMARY ------- {summary} REPOSITORY STRUCTURE ------------------- {tree} SOURCE CODE ----------- {content} """ + try: # Initialize and run analysis agents = PraisonAIAgents( agents=[code_analyzer], tasks=[code_analysis_task] ) return agents.start(context_text) + except Exception as e: + raise RuntimeError(f"Analysis failed: {str(e)}")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.def analyze_code(code_source: str) -> CodeAnalysisReport: """ Analyze code from directory path or GitHub URL """ try: # Ingest code content summary, tree, content = ingest(code_source) except Exception as e: raise ValueError(f"Failed to ingest code from {code_source}: {str(e)}") if not content or not content.strip(): raise ValueError("No code content found to analyze") # Concatenate context into structured format context_text = f""" CODE REPOSITORY ANALYSIS ======================= SUMMARY ------- {summary} REPOSITORY STRUCTURE ------------------- {tree} SOURCE CODE ----------- {content} """ try: # Initialize and run analysis agents = PraisonAIAgents( agents=[code_analyzer], tasks=[code_analysis_task] ) return agents.start(context_text) except Exception as e: raise RuntimeError(f"Analysis failed: {str(e)}")🤖 Prompt for AI Agents
In examples/cookbooks/Code_Analysis_Agent.ipynb around lines 191 to 222, the analyze_code function lacks error handling for potential failures during code ingestion or agent execution. Add try-except blocks around the ingest call and the agents.start call to catch exceptions, log or handle errors gracefully, and possibly return a meaningful error response or fallback result to improve robustness.
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Actionable comments posted: 2
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examples/cookbooks/Code_Analysis_Agent.ipynb(2 hunks)examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb(1 hunks)
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- GitHub Check: test-core (3.11)
🔇 Additional comments (2)
examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb (2)
278-283: LGTM! Model loading implementation follows best practices.The model loading code properly uses:
torch.float16for memory efficiencydevice_map="auto"for automatic device placement- Correct model identifier for TinyLlama
342-344: Investigate the incomplete example output.The example output only shows the input prompt without any generated response, suggesting potential issues with:
- Generation parameters not producing new tokens
- Model configuration problems
- Tokenizer issues
The incomplete output suggests the model may not be generating new tokens. Please test the function with different prompts and generation parameters to ensure it works as expected. Consider adding a more explicit prompt format for the TinyLlama-Chat model.
For TinyLlama-Chat models, try using a more structured prompt format:
prompt = "<|system|>\nYou are a helpful assistant.</s>\n<|user|>\nWhat is the capital of France?</s>\n<|assistant|>\n"
| "def generate_response(prompt, max_length=256):\n", | ||
| " inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n", | ||
| " outputs = model.generate(**inputs, max_new_tokens=max_length)\n", | ||
| " return tokenizer.decode(outputs[0], skip_special_tokens=True)" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Fix parameter naming and improve the response generation function.
The function has several issues:
- Parameter
max_lengthis misleading since it's used asmax_new_tokens - Missing error handling for edge cases
- Returns the full sequence including the original prompt
- No padding token handling
-def generate_response(prompt, max_length=256):
- inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
- outputs = model.generate(**inputs, max_new_tokens=max_length)
- return tokenizer.decode(outputs[0], skip_special_tokens=True)
+def generate_response(prompt, max_new_tokens=256):
+ if not prompt.strip():
+ return "Error: Empty prompt provided"
+
+ try:
+ inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(model.device)
+ outputs = model.generate(
+ **inputs,
+ max_new_tokens=max_new_tokens,
+ do_sample=True,
+ temperature=0.7,
+ pad_token_id=tokenizer.eos_token_id
+ )
+ # Return only the newly generated tokens
+ response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
+ return response.strip()
+ except Exception as e:
+ return f"Error generating response: {str(e)}"📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "def generate_response(prompt, max_length=256):\n", | |
| " inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n", | |
| " outputs = model.generate(**inputs, max_new_tokens=max_length)\n", | |
| " return tokenizer.decode(outputs[0], skip_special_tokens=True)" | |
| def generate_response(prompt, max_new_tokens=256): | |
| if not prompt.strip(): | |
| return "Error: Empty prompt provided" | |
| try: | |
| inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(model.device) | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=max_new_tokens, | |
| do_sample=True, | |
| temperature=0.7, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| # Return only the newly generated tokens | |
| response = tokenizer.decode( | |
| outputs[0][inputs['input_ids'].shape[1]:], | |
| skip_special_tokens=True | |
| ) | |
| return response.strip() | |
| except Exception as e: | |
| return f"Error generating response: {str(e)}" |
🤖 Prompt for AI Agents
In examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb around lines 305
to 308, rename the parameter from max_length to max_new_tokens to accurately
reflect its use in model.generate. Add error handling to manage edge cases such
as empty prompts or generation failures. Modify the return statement to exclude
the original prompt from the decoded output, returning only the newly generated
tokens. Also, ensure the tokenizer's padding token is set or handled properly to
avoid generation issues.
| "id": "qS0M9pRhTGpf" | ||
| }, | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb)" |
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💡 Verification agent
🧩 Analysis chain
Verify the GitHub repository path in the Colab badge.
The Colab badge references DhivyaBharathy-web/PraisonAI which appears to be a personal fork. Ensure this path will remain valid after the PR is merged, or update it to point to the main repository.
🏁 Script executed:
#!/bin/bash
# Check if this is the correct repository structure and verify the intended final location
echo "Current repository remote URLs:"
git remote -v
echo ""
echo "Checking if the referenced path matches the current repository structure:"
if git log --oneline | head -5; then
echo "Repository appears to be accessible"
else
echo "Could not access repository log"
fiLength of output: 663
Update Colab badge to reference the main repository
The Colab badge in examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb still points at the fork (DhivyaBharathy-web/PraisonAI), but the remote is MervinPraison/PraisonAI. Please update the link at line 29:
- File: examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb
- Line: 29
- [](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb)
+ [](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb)This ensures the badge remains valid once the notebook is merged into the main repo.
📝 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/TinyLlama_1_1B_model_SimpleAIAgent.ipynb)" | |
| "[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb)" |
🤖 Prompt for AI Agents
In examples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb at line 29, the
Colab badge URL references the forked repository DhivyaBharathy-web/PraisonAI.
Update this URL to point to the main repository MervinPraison/PraisonAI to
ensure the badge remains valid after merging. Replace the GitHub path in the
badge link accordingly.
2 participants
User description
This agent uses the TinyLlama-1.1B model to generate code or responses based on user input prompts. It demonstrates a minimal setup for building an AI assistant using Hugging Face Transformers with a lightweight language model.
PR Type
Documentation
Description
Added five new Jupyter notebooks in the
examples/cookbooksdirectory, each demonstrating practical AI agent use cases with detailed instructions and code examples.Introduced a notebook for using the TinyLlama-1.1B model as a simple AI agent, including setup, response generation, and usage demonstration.
Added a comprehensive code analysis agent notebook, featuring structured reporting with Pydantic schemas and example analysis workflows.
Provided a predictive maintenance workflow notebook showcasing multi-agent orchestration for sensor data analysis, anomaly detection, and maintenance scheduling.
Included a beginner-friendly notebook for the Qwen2.5-0.5B-Instruct model, guiding users through chat-based generation tasks.
Added a Gemma 2B instruction agent notebook, covering model setup, prompt configuration, inference, and model saving for instruction following and code generation.
Changes walkthrough 📝
TinyLlama_1_1B_model_SimpleAIAgent.ipynb
Add TinyLlama-1.1B model agent demo notebook with usage exampleexamples/cookbooks/TinyLlama_1_1B_model_SimpleAIAgent.ipynb
TinyLlama-1.1B model as a simple AI agent.
(
transformers,accelerate,torch).generate_response) for generating responsesfrom the model.
Code_Analysis_Agent.ipynb
Add notebook for AI-powered code analysis agent with structuredreportingexamples/cookbooks/Code_Analysis_Agent.ipynb
for code analysis and quality assessment.
ingestion using
gitingest.CodeAnalysisReport) forstructured code analysis output.
on a codebase, and sample output display.
display.
Predictive_Maintenance_Multi_Agent_Workflow.ipynb
Add predictive maintenance workflow notebook with multi-agentorchestrationexamples/cookbooks/Predictive_Maintenance_Multi_Agent_Workflow.ipynb
workflow using multiple AI agents.
analysis, anomaly detection, failure prediction, and maintenance
scheduling.
praisonaiagentslibrary.planning.
Qwen2_5_InstructionAgent.ipynb
Add Qwen2.5 instruction agent notebook for simple chat generationexamples/cookbooks/Qwen2_5_InstructionAgent.ipynb
model for chat-based generation.
loading, and prompt preparation.
print the output.
Gemma2B_Instruction_Agent.ipynb
Add Gemma 2B instruction agent notebook with data prep and inferenceexamples/cookbooks/Gemma2B_Instruction_Agent.ipynb
gemma-2b-itmodel for instructionfollowing and code generation.
Face authentication.
and model saving.
Summary by CodeRabbit