Add mistral7b agent

[Dhivya-Bharathy]

User description

MistralTechAgent is a simple and efficient AI assistant powered by the Mistral-7B-Instruct model. It is designed to provide accurate and easy-to-understand answers to technical questions. This agent uses a lightweight instruction-tuned model that runs smoothly on limited hardware while delivering high-quality responses. It also supports a customizable prompt format for structured queries.

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PR Type

enhancement, documentation

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Description

• Added a new Jupyter notebook for a Groq Llama3-8b-8192 AI assistant agent.

• Demonstrates structured agent setup with YAML config and prompt templates.

• Provides step-by-step instructions, code, and example output for Groq model usage.

• Includes Colab integration and detailed markdown explanations.

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Changes walkthrough 📝

Relevant files

Enhancement

Groq_LPU_Powered_AI_Assistant.ipynb Add Groq Llama3-8b-8192 agent example notebook with YAML config examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb Introduces a new notebook for a Groq-powered AI assistant agent. Shows dependency installation and Groq SDK initialization. Demonstrates YAML-based configuration and prompt construction. Provides example usage, output, and markdown documentation. +245/-0  MistralTechAgent.ipynb Add Mistral-7B-Instruct technical assistant notebook          examples/cookbooks/MistralTechAgent.ipynb Adds a new notebook for a Mistral-7B-Instruct-based technical Q&A agent. Demonstrates usage, prompt customization, and model integration. Provides documentation and example workflow for technical assistance. +4383/-0

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Summary by CodeRabbit

• New Features
  • Introduced a Jupyter notebook example demonstrating how to build an AI assistant powered by Groq's Llama3-8b-8192 model, including setup instructions and a sample Q&A workflow.
  • Added a Jupyter notebook example showcasing a lightweight technical assistant using the Mistral-7B-Instruct model, with fallback to a smaller model for broader hardware compatibility and customizable prompt templates.
• Documentation
  • Included step-by-step explanations and usage examples in both notebooks, along with Colab badges for easy execution.

[coderabbitai[bot]]

Walkthrough

Two new Jupyter notebook examples are added demonstrating AI assistants: one powered by Groq's Llama3-8b-8192 model with Groq client integration, and another using the Mistral-7B-Instruct model with fallback to distilgpt2, including setup, prompt construction, model loading, and example queries.

Changes

File(s)	Change Summary
examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb	Added notebook example for Groq Llama3-8b-8192 AI assistant; includes dependency installation, Groq client setup, prompt building, and sample usage.
examples/cookbooks/MistralTechAgent.ipynb	Added notebook example for MistralTechAgent using Mistral-7B-Instruct model with fallback, environment setup, prompt template, model loading, and chat method.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant GroqNotebook as Groq Notebook
    participant GroqClient

    User->>GroqNotebook: Provide question
    GroqNotebook->>GroqNotebook: build_prompt(user_question)
    GroqNotebook->>GroqClient: run_groq_chat(prompt_messages, model)
    GroqClient-->>GroqNotebook: Return model response
    GroqNotebook->>User: Display detailed answer and summary

sequenceDiagram
    participant User
    participant MistralNotebook as Mistral Notebook
    participant Model

    User->>MistralNotebook: Provide question
    MistralNotebook->>Model: Load Mistral-7B-Instruct (fallback to distilgpt2 if fails)
    MistralNotebook->>Model: chat(prompt)
    Model-->>MistralNotebook: Return generated response
    MistralNotebook->>User: Display detailed answer

Possibly related PRs

• MervinPraison/PraisonAI#622: Adds the same Groq Llama3-8b-8192 Agent notebook with identical functions run_groq_chat and build_prompt in examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb.

Poem

Two agents hop into the scene,
Groq and Mistral, sharp and keen.
Prompts are built, models awake,
Questions answered, no mistake.
From GPUs to clever code,
AI paths are freshly sowed!
🐇🤖✨

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[qodo-code-review[bot]]

PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 2 🔵🔵⚪⚪⚪

🧪 No relevant tests

🔒 Security concerns Sensitive information exposure: The notebook includes a line where users are instructed to directly enter their Groq API key in the code (line 86: os.environ['GROQ_API_KEY'] = 'enter your key'). This approach could lead to accidental exposure of API keys if users commit their notebooks with actual keys. A better approach would be to use environment variables or a secure configuration file that's excluded from version control.

⚡ Recommended focus areas for review API Key Exposure The notebook contains a placeholder for the Groq API key that instructs users to "enter your key" directly in the code. This approach could lead to accidental API key commits if users save their notebooks with actual keys. "os.environ['GROQ_API_KEY'] = 'enter your key'\n", "\n", Colab Link Mismatch The Colab link points to a different GitHub repository (DhivyaBharathy-web/PraisonAI) than what would be expected for this project, which may cause confusion for users trying to open the notebook in Colab. "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb)\n" ],

[qodo-code-review[bot]]

PR Code Suggestions ✨

Explore these optional code suggestions:

Category	Suggestion	Impact
General	Remove unused parameters The temperature parameter is set to 1.0 while do_sample is False, which is inconsistent. When do_sample=False, temperature has no effect as the model uses greedy decoding. Either set do_sample=True to use temperature, or remove the temperature parameter for clarity. examples/cookbooks/MistralTechAgent.ipynb [539-551] def chat(self, prompt: str, max_new_tokens=256) -> str: inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device) with torch.no_grad(): outputs = self.model.generate( **inputs, max_new_tokens=max_new_tokens, do_sample=False, # DETERMINISTIC output - temperature=1.0, - top_p=1.0, pad_token_id=self.tokenizer.eos_token_id ) full_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True) return full_output[len(prompt):].strip() [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 6 __ Why: The suggestion correctly identifies that temperature and top_p parameters are ignored when do_sample=False. Removing these parameters improves code clarity and avoids confusion.	Low
Security	Secure API key handling Hardcoding API keys directly in the notebook is a security risk. Use a more secure approach like environment variables or a configuration file that isn't committed to version control, or prompt users to input their key at runtime. examples/cookbooks/MistralTechAgent.ipynb [108] -os.environ["HF_TOKEN"] = "Enter your hugging api key" +# Option 1: Prompt user for API key +import getpass +os.environ["HF_TOKEN"] = getpass.getpass("Enter your Hugging Face API key: ") +# Option 2: Load from environment variable if available +# os.environ["HF_TOKEN"] = os.environ.get("HF_TOKEN") or getpass.getpass("Enter your Hugging Face API key: ") + [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 5 __ Why: While the suggestion promotes good security practices, the current code only contains a placeholder string. For a demo notebook, the security risk is minimal, though the improvement is still valid.	Low
	Remove hardcoded API key Hardcoding API keys directly in code is a security risk. Instead, use environment variables or a secure configuration method to provide the API key at runtime. examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb [85-86] -# 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 +# Make sure to set GROQ_API_KEY in your environment before running +# os.environ['GROQ_API_KEY'] = 'your_key_here' # Uncomment and set only for local testing [To ensure code accuracy, apply this suggestion manually] Suggestion importance[1-10]: 4 __ Why: While the security concern is valid, the value 'enter your key' is clearly a placeholder in this tutorial notebook. The existing comment already advises users to replace it or use environment variables, making this appropriate for example code.	Low
[ ] Update

[codecov[bot]]

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Project coverage is 0.00%. Comparing base (39e03a4) to head (6fcf84e). Report is 43 commits behind head on main.

:exclamation: There is a different number of reports uploaded between BASE (39e03a4) and HEAD (6fcf84e). Click for more details.

HEAD has 1 upload less than BASE

Flag	BASE (39e03a4)	HEAD (6fcf84e)
unit-tests	1	0

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #625       +/-   ##
==========================================
- 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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quick-validation	0.00% <ø> (ø)
unit-tests	?

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