Add Groq Llama3-8b-8192 Agent notebook #622
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WalkthroughA new Jupyter notebook example is added to demonstrate building an AI assistant using Groq's llama3-8b-8192 model. The notebook covers dependency installation, API setup, prompt construction, and running a sample query, with explanatory markdown and helper functions for interacting with the Groq API. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Notebook
participant GroqAPI
User->>Notebook: Provide question
Notebook->>Notebook: build_prompt(user_question)
Notebook->>GroqAPI: run_groq_chat(prompt_messages, model)
GroqAPI-->>Notebook: Return response
Notebook->>User: Display detailed answer and summary
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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 a new Jupyter notebook (.ipynb) that serves as a cookbook example. The primary goal is to demonstrate how to build an AI assistant using the Groq API, specifically leveraging the llama3-8b-8192 model. The notebook showcases a structured approach, utilizing YAML for configuration and prompt templating, making it easy to understand and potentially adapt for other Groq models or use cases. It includes all the necessary steps from installing dependencies to running an example query and displaying the output.
Highlights
- New Cookbook Example: Adds a new Jupyter notebook (
examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb) to provide a practical example of using the Groq API. - Groq API Integration: Demonstrates how to initialize the Groq client and make chat completion calls using the
llama3-8b-8192model. - Structured Prompting: Utilizes a YAML configuration string within the notebook to define the model and a prompt template, promoting a clean separation of concerns.
- End-to-End Example: Includes steps for dependency installation, API setup, prompt construction, model execution, and displays the resulting output for a sample question.
Changelog
- examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb
- Added a new Jupyter notebook demonstrating a Groq Llama3-8b-8192 powered AI assistant.
- Includes code to install
groqandpyyamldependencies. - Sets up the Groq client and defines a helper function
run_groq_chat. - Configures the model and prompt template using an embedded YAML string.
- Provides a
build_promptfunction to format user questions into the required message structure. - Executes an example query about Groq's LPU technology and prints the model's response.
- Includes the expected output from the example run.
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Actionable comments posted: 3
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- GitHub Check: GitGuardian Security Checks
🔇 Additional comments (4)
examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb (4)
57-59: LGTM!The dependency installation is correctly implemented with appropriate packages for the Groq API and YAML configuration parsing.
119-129: LGTM!The YAML configuration is well-structured with clear prompt instructions that align with the notebook's objectives of providing detailed answers and summaries.
152-158: LGTM!The build_prompt function correctly formats the template with user input and creates the proper message structure for the Groq chat API.
212-216: LGTM!The main execution logic correctly demonstrates the agent functionality with an appropriate sample question and proper use of the helper functions and configuration.
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb)\n" |
There was a problem hiding this comment.
Fix the repository reference in the Colab URL.
The Colab badge URL references the contributor's fork instead of the main repository. This should be updated to point to the correct repository.
Apply this diff to fix the repository reference:
- "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb)\n"
+ "[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.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/Groq_LPU_Powered_AI_Assistant.ipynb)\n" | |
| "[](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb)\n" |
🤖 Prompt for AI Agents
In examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb at line 18, the Colab
badge URL incorrectly points to the contributor's fork repository. Update the
URL to reference the main repository by replacing the fork's GitHub path with
the main repository's correct path to ensure the badge links to the intended
notebook.
| "# Set API key (replace with your own or use environment variables)\n", | ||
| "os.environ['GROQ_API_KEY'] = 'enter your key'\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve API key security guidance.
The hard-coded placeholder 'enter your key' could lead to security issues if users accidentally commit their actual API keys. Consider providing better guidance on secure API key management.
Apply this diff to improve the API key setup:
-# Set API key (replace with your own or use environment variables)
-os.environ['GROQ_API_KEY'] = 'enter your key'
+# Set API key from environment variable for security
+# Before running this notebook, set your API key:
+# export GROQ_API_KEY="your_actual_api_key_here"
+if 'GROQ_API_KEY' not in os.environ:
+ raise ValueError("Please set the GROQ_API_KEY environment variable")📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "# Set API key (replace with your own or use environment variables)\n", | |
| "os.environ['GROQ_API_KEY'] = 'enter your key'\n", | |
| # Set API key from environment variable for security | |
| # Before running this notebook, set your API key: | |
| # export GROQ_API_KEY="your_actual_api_key_here" | |
| if 'GROQ_API_KEY' not in os.environ: | |
| raise ValueError("Please set the GROQ_API_KEY environment variable") |
🤖 Prompt for AI Agents
In examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb around lines 85 to 86,
the API key is set using a hard-coded placeholder string which risks accidental
exposure if committed. Replace the hard-coded key with instructions to load the
API key securely from environment variables or a secure vault, and update the
comment to guide users to set their API key outside the code, such as via
environment variables, to improve security.
| "def run_groq_chat(prompt_messages, model='llama3-8b-8192'):\n", | ||
| " response = client.chat.completions.create(\n", | ||
| " model=model,\n", | ||
| " messages=prompt_messages\n", | ||
| " )\n", | ||
| " return response.choices[0].message.content\n" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling to the API function.
The function lacks error handling for potential API failures, network issues, or unexpected response structures. This could cause the notebook to crash unexpectedly.
Apply this diff to add error handling:
def run_groq_chat(prompt_messages, model='llama3-8b-8192'):
- response = client.chat.completions.create(
- model=model,
- messages=prompt_messages
- )
- return response.choices[0].message.content
+ try:
+ response = client.chat.completions.create(
+ model=model,
+ messages=prompt_messages
+ )
+ if not response.choices:
+ raise ValueError("No response choices returned from API")
+ return response.choices[0].message.content
+ except Exception as e:
+ raise RuntimeError(f"Failed to get response from Groq API: {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 run_groq_chat(prompt_messages, model='llama3-8b-8192'):\n", | |
| " response = client.chat.completions.create(\n", | |
| " model=model,\n", | |
| " messages=prompt_messages\n", | |
| " )\n", | |
| " return response.choices[0].message.content\n" | |
| def run_groq_chat(prompt_messages, model='llama3-8b-8192'): | |
| try: | |
| response = client.chat.completions.create( | |
| model=model, | |
| messages=prompt_messages | |
| ) | |
| if not response.choices: | |
| raise ValueError("No response choices returned from API") | |
| return response.choices[0].message.content | |
| except Exception as e: | |
| raise RuntimeError(f"Failed to get response from Groq API: {e}") |
🤖 Prompt for AI Agents
In examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb around lines 91 to 96,
the run_groq_chat function lacks error handling for API failures, network
issues, or unexpected response structures. Wrap the API call in a try-except
block to catch exceptions, handle errors gracefully, and return a meaningful
error message or fallback value instead of letting the notebook crash.
There was a problem hiding this comment.
Code Review
This pull request introduces a new Jupyter notebook, Groq_LPU_Powered_AI_Assistant.ipynb, demonstrating how to use Groq's Llama3-8b-8192 model. The notebook is well-structured, with clear explanations in markdown and functional code cells that showcase YAML configuration for prompts, interaction with the Groq Python SDK, and output formatting. This is a valuable addition for users looking to get started with Groq.
My review focuses on enhancing security, robustness, and clarity. The most critical point is the handling of API keys, which needs to be addressed to prevent accidental key exposure. Additionally, improving error handling in API calls and clarifying prompt construction would make the example more robust and user-friendly.
Summary of Findings
- API Key Security: The notebook includes a hardcoded placeholder for the
GROQ_API_KEY, which is a security risk as it might lead to accidental exposure of real keys. This needs to be replaced with safer key management practices. - Error Handling in API Calls: The
run_groq_chatfunction lacks robust error handling for API responses, potentially leading to runtime errors if the API returns unexpected data or fails. Adding checks for response validity would improve robustness. - Redundant System Prompt: The
build_promptfunction uses a generic system message that appears redundant given that the main prompt template (from YAML) already defines a specific persona for the AI assistant. Consolidating this would improve clarity.
Merge Readiness
This pull request adds a very useful example notebook for the Groq Llama3 agent. The structure and explanations are generally excellent. However, there is a critical security issue related to API key handling (hardcoding a placeholder) that must be addressed before this PR can be considered for merging. Additionally, there are suggestions for improving error handling and prompt clarity that would enhance the notebook's quality and robustness for users.
As a reviewer, I am not authorized to approve pull requests. I strongly recommend that the author addresses the critical API key issue and considers the other feedback. Further review and approval by authorized maintainers will be necessary after these changes are made.
| "from groq import Groq\n", | ||
| "\n", | ||
| "# Set API key (replace with your own or use environment variables)\n", | ||
| "os.environ['GROQ_API_KEY'] = 'enter your key'\n", |
There was a problem hiding this comment.
Hardcoding API keys, even as a placeholder like 'enter your key', poses a significant security risk. Users might inadvertently commit their actual keys to version control. It's crucial to guide users towards more secure practices for handling API keys, especially in example notebooks.
Could we replace this direct assignment with instructions and a safer method, such as prompting the user if the environment variable GROQ_API_KEY is not set, or guiding them to use Colab secrets if applicable? The Groq client typically reads the API key from the GROQ_API_KEY environment variable automatically.
# It is strongly recommended to set GROQ_API_KEY as an environment variable
# or use a secret management tool (e.g., Colab secrets) instead of hardcoding.
# The Groq client will automatically detect GROQ_API_KEY if set in the environment.
# Example: export GROQ_API_KEY='your_key_here' OR use Colab secrets.
groq_api_key_env = os.getenv("GROQ_API_KEY")
if not groq_api_key_env or groq_api_key_env == 'enter your key':
print("GROQ_API_KEY not found or is a placeholder. Please set it securely.")
# For interactive use, you could prompt for the key if not set:
# try:
# import getpass
# key_to_set = getpass.getpass("Enter your Groq API Key: ")
# if key_to_set:
# os.environ['GROQ_API_KEY'] = key_to_set
# else:
# print("API Key not entered.")
# except (ImportError, RuntimeError): # RuntimeError in non-interactive environments
# print("getpass not available in this environment.")
# The Groq() client below will use the environment variable.
| " response = client.chat.completions.create(\n", | ||
| " model=model,\n", | ||
| " messages=prompt_messages\n", | ||
| " )\n", | ||
| " return response.choices[0].message.content\n" |
There was a problem hiding this comment.
The run_groq_chat function directly accesses response.choices[0].message.content. If the API call fails or returns an unexpected structure (e.g., an empty choices list or no message.content), this will raise an error (like IndexError or AttributeError).
To make this example more robust and user-friendly, would it be beneficial to add some basic error handling? This could involve checking if response.choices is populated and if message.content exists before trying to access it, and perhaps printing a helpful error message or returning a specific error indicator.
try:
response = client.chat.completions.create(
model=model,
messages=prompt_messages
)
if response.choices and len(response.choices) > 0 and response.choices[0].message and response.choices[0].message.content:
return response.choices[0].message.content
else:
print(f"Warning: Received an empty or unexpected response from Groq API. Full response: {response}")
return "Error: No valid content received from API."
except Exception as e:
print(f"An error occurred while calling the Groq API: {e}")
return f"Error communicating with Groq API: {str(e)}"
| "def build_prompt(user_question):\n", | ||
| " prompt_text = config['prompt_template'].format(user_question=user_question)\n", | ||
| " messages = [\n", | ||
| " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", |
There was a problem hiding this comment.
The build_prompt function includes a generic system message: {"role": "system", "content": "You are a helpful assistant."}. However, the prompt_template loaded from the YAML configuration (defined in lines 121-127) already specifies a more detailed persona for the AI: "You are an expert AI assistant knowledgeable about Groq's technology."
Having both might be redundant or could lead to confusion about which system instruction takes precedence. Consider removing the generic system message here and relying on the persona defined within the prompt_text that's constructed from the YAML template. This would simplify the prompt structure and ensure clarity.
# {"role": "system", "content": "You are a helpful assistant."}, # Consider removing this line, as the AI's persona is already defined in the YAML prompt_template (which becomes part of prompt_text).
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #622 +/- ##
==========================================
- Coverage 16.43% 0.00% -16.44%
==========================================
Files 24 22 -2
Lines 2160 1980 -180
Branches 302 0 -302
==========================================
- Hits 355 0 -355
- Misses 1789 1980 +191
+ Partials 16 0 -16
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User description
This agent uses Groq’s LPU-powered llama3-8b-8192 model to answer user questions with detailed explanations and concise summaries. It dynamically builds prompts from a YAML template and interacts with the Groq API to generate responses. The design cleanly separates configuration, prompt construction, model interaction, and output formatting for easy customization.
PR Type
Enhancement, Documentation
Description
Changes walkthrough 📝
Groq_LPU_Powered_AI_Assistant.ipynb
New Groq Llama3-8b-8192 agent notebook with YAML configexamples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb
output formatting.
Summary by CodeRabbit