lead generation Composio Error (YET TO COMPLETE) #150
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WalkthroughThe changes update the agent creation functions within the lead generation module. The Changes
Sequence Diagram(s)sequenceDiagram
participant Client
participant SheetsWriter as write_to_google_sheets
participant GoogleAPI as GoogleSheetsAPI
Client->>SheetsWriter: Submit flattened_data
alt flattened_data is empty
SheetsWriter-->>Client: Log warning and return
else flattened_data is present
SheetsWriter->>SheetsWriter: Log record count and sample record
SheetsWriter->>GoogleAPI: Request to create a Google Sheet
GoogleAPI-->>SheetsWriter: Return API response
SheetsWriter->>SheetsWriter: Log API response
alt Response contains valid link
SheetsWriter-->>Client: Return sheet link
else Response invalid
SheetsWriter->>SheetsWriter: Log detailed error and traceback
SheetsWriter-->>Client: Return error message
end
end
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Actionable comments posted: 1
🧹 Nitpick comments (3)
ai_agent_tutorials/ai_lead_generation_agent/ai_lead_generation_agent.py (3)
111-115: Consider controlling debug output in productionThese debugging statements are helpful during development but might expose sensitive data in production. Consider adding a debug flag or using proper logging levels.
- # Debug the data structure - st.write(f"Data to write: {len(flattened_data)} records") - if len(flattened_data) > 0: - st.write("Sample record:", flattened_data[0]) + # Debug the data structure + debug_mode = st.session_state.get("debug_mode", False) + if debug_mode: + st.write(f"Data to write: {len(flattened_data)} records") + if len(flattened_data) > 0: + st.write("Sample record:", flattened_data[0])
128-130: Debug output should be conditionalSimilar to the previous comment, this debugging output should be controlled to prevent showing sensitive data in production.
- # Add debugging output to see the actual response - st.write("Google Sheets API Response:", create_sheet_response.content) + # Add debugging output to see the actual response + if st.session_state.get("debug_mode", False): + st.write("Google Sheets API Response:", create_sheet_response.content)
134-142: Improved error handling, but move import statementThe enhanced error handling with detailed messages and traceback is good for debugging. However, the
tracebackmodule should be imported at the top of the file, not inside the exception handler.+ import traceback # Add this at the top of the file with other imports # Later in the code: except Exception as e: # Improve error handling to see what's going wrong st.error(f"Error creating Google Sheet: {str(e)}") - import traceback st.error(traceback.format_exc())
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ai_agent_tutorials/ai_lead_generation_agent/ai_lead_generation_agent.py(2 hunks)
🔇 Additional comments (3)
ai_agent_tutorials/ai_lead_generation_agent/ai_lead_generation_agent.py (3)
94-94: Change in tool retrieval approach looks goodThe way you're retrieving the Google Sheets tool has been improved to directly use the result from
get_tools()without indexing.
106-110: Good defensive coding with data validationAdding validation to check for empty data before proceeding is a good practice that prevents errors and provides clear feedback to the user.
147-148: Consistent model and parameter updatesYou've made similar changes as in the
create_google_sheets_agentfunction, upgrading the model and updating the parameter name. This ensures consistency across your agent implementations.
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| google_sheets_agent = Agent( | ||
| model=OpenAIChat(id="gpt-4o-mini", api_key=openai_api_key), | ||
| model=OpenAIChat(id="gpt-4o", api_key=openai_api_key), |
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💡 Verification agent
🧩 Analysis chain
Model upgraded and parameter name updated
You've upgraded from gpt-4o-mini to gpt-4o and changed from system_prompt to description. This should improve the agent's capabilities, though at potentially higher cost.
Note: There's a discrepancy between the AI summary (which mentions gpt-4) and the actual implementation (which uses gpt-4o).
Also applies to: 100-100
🌐 Web query:
What are the differences between gpt-4o-mini, gpt-4o, and gpt-4 models in terms of capabilities and pricing?
💡 Result:
OpenAI's GPT-4 series offers three distinct models with varying capabilities and pricing structures optimized for different use cases:
Model Overview
| Feature | GPT-4 | GPT-4o | GPT-4o Mini |
|---|---|---|---|
| Parameters | ~1 trillion [2] | 175 billion [4] | 1.5 billion [4] |
| Context Window | 128k tokens [11] | 128k tokens [1] | 128k tokens [12] |
| Max Output Tokens | 8,192 tokens [11] | 16,384 tokens [1] | 16,384 tokens [12] |
| Multimodal Support | Text + limited images [6] | Text, images, audio, video [8] | Text + vision (expanding to audio/video) [16] |
Key Differences in Capabilities
GPT-4
- Focuses on advanced reasoning and creative tasks[6]
- Handles complex problem-solving scenarios[6]
- Limited to text and basic image inputs[16]
GPT-4o
- Supports full multimodal interactions (audio/video processing)[6][8]
- 2× faster response speed than GPT-4 (134.9 tokens/sec vs. 39.3)[1][5]
- Superior accuracy in nuanced contexts (87.2 HumanEval score vs. GPT-4's 67.0)[11][14]
GPT-4o Mini
- Optimized for speed and cost-efficiency (112.2 tokens/sec)[4][12]
- Excels at routine tasks like customer support and receipt processing[12]
- Reduced depth in complex analysis compared to full GPT-4o[4]
Pricing Comparison
| Model | Input Cost ($/1M tokens) | Output Cost ($/1M tokens) |
|---|---|---|
| GPT-4 | $30 [13] | $60 [13] |
| GPT-4 Turbo | $10 [15] | $30 [1] |
| GPT-4o | $2.50 [3] | $10 [3] |
| GPT-4o Mini | $0.15 [3] | $0.60 [3] |
Key pricing facts:
- GPT-4o Mini costs 200× less than GPT-4 for input tokens [11]
- GPT-4o is 60% cheaper than GPT-3.5 Turbo while outperforming it [12]
Use Case Recommendations
- Research/Specialized Tasks: GPT-4o (highest accuracy)[4]
- High-Volume Applications: GPT-4o Mini (cost-optimized)[4][20]
- Legacy Systems: GPT-4 Turbo (balanced speed/cost)[1]
Performance benchmarks show GPT-4o achieving 90.2% HumanEval score (coding) and 75.9% MATH benchmark score, outperforming earlier models in analytical tasks[5][11]. Meanwhile, GPT-4o Mini delivers 82% MMLU score, surpassing GPT-3.5 Turbo's 69.8%[12]. For most developers, GPT-4o Mini provides the best price-to-performance ratio unless advanced multimodal features are required[20].
Citations:
- 1: https://teamai.com/blog/large-language-models-llms/understanding-different-chatgpt-models/
- 2: https://gettalkative.com/info/gpt-models-compared
- 3: https://openai.com/api/pricing/
- 4: https://anthemcreation.com/en/artificial-intelligence/comparative-gpt-4o-gpt-4o-mini-open-ai/
- 5: https://docsbot.ai/models/compare/gpt-4o/gpt-4-5
- 6: https://blog.promptlayer.com/understanding-gpt-4o-vs-gpt-4-a-comparative-guide/
- 7: https://artificialanalysis.ai/models/gpt-4
- 8: https://timesofindia.indiatimes.com/technology/tech-tips/openais-gpt-4o-vs-gpt-4o-mini-which-ai-model-to-use-and-why/articleshow/111927368.cms
- 9: https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/
- 10: https://www.bureauworks.com/blog/gpt-4-vs-gpt-3-openai-models-comparison-and-potential-when-it-comes-to-translation
- 11: https://context.ai/compare/gpt-4o-mini/gpt-4
- 12: https://www.techtarget.com/whatis/feature/GPT-4o-explained-Everything-you-need-to-know
- 13: https://www.techtarget.com/searchenterpriseai/tip/GPT-35-vs-GPT-4-Biggest-differences-to-consider
- 14: https://neoteric.eu/blog/gpt-4o-vs-gpt-4-vs-gpt-3-5-comparison-in-real-world-scenarios/
- 15: https://www.nebuly.com/blog/openai-gpt-4-api-pricing
- 16: https://www.techtarget.com/searchenterpriseai/feature/GPT-4o-vs-GPT-4-How-do-they-compare
- 17: https://docsbot.ai/models/compare/gpt-4o/o1
- 18: https://help.openai.com/en/articles/7127966-what-is-the-difference-between-the-gpt-4-model-versions
- 19: https://www.vantage.sh/blog/gcp-google-gemini-vs-azure-openai-gpt-ai-cost
- 20: https://www.youreverydayai.com/gpt-4o-mini-review-and-gpt-4o-mini-vs-gpt-4o/
Model upgrade confirmed – please update the AI summary for consistency
The upgrade from using gpt-4o-mini to gpt-4o and renaming the parameter from system_prompt to description is correctly implemented. This change leverages GPT-4o’s enhanced multimodal capabilities and improved performance (e.g., faster response speed and greater nuanced accuracy), as seen in recent comparisons.
However, note that the AI summary still refers to gpt-4 rather than gpt-4o. Please update the AI summary (and any related documentation) to match the new implementation, ensuring consistency across the project.
Action Items:
- Update the AI summary to refer to
gpt-4oinstead ofgpt-4. - Confirm that similar changes are applied where the model is referenced (e.g., at line 100).
Summary by CodeRabbit
New Features
Refactor