wehub-resource-sync
61 lines
1.9 KiB
Markdown
61 lines
1.9 KiB
Markdown
# Parlant Guidelines vs Traditional LLM Prompt: Life Insurance Agent Demo
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This project demonstrates the advantages of **Parlant's structured approach** over traditional monolithic LLM prompts for building conversational agents.
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## Quick Start
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**Terminal 1 - Start the server:**
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```bash
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uv run parlant_agent_server.py
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```
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**Terminal 2 - Run the comparison:**
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```bash
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uv run demo_comparison.py
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```
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## Demo Queries
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The demo tests 5 realistic scenarios:
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- Policy replacement with critical warnings
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- Coverage calculation with specific parameters
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- Health condition impact assessment
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- Mixed topics with boundary maintenance
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- Decision making with conflicting rules
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## Project Structure
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```
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parlant-conversational-agent/
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├── parlant_agent_server.py # Parlant agent with tools & guidelines
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├── demo_comparison.py # Main comparison demo runner
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├── traditional_llm_prompt.py # Monolithic prompt approach
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├── parlant_client_utils.py # Parlant API client utilities
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├── rich_table_formatter.py # Beautiful console table rendering
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└── pyproject.toml # Project dependencies (uv)
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```
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## Setup
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```bash
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uv sync # Install dependencies
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```
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## Requirements
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- Python 3.10+ (required for Parlant)
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- `uv` package manager
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- OpenAI API key in `.env` file
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## 📬 Stay Updated with Our Newsletter!
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**Get a FREE Data Science eBook** 📖 with 150+ essential lessons in Data Science when you subscribe to our newsletter! Stay in the loop with the latest tutorials, insights, and exclusive resources. [Subscribe now!](https://join.dailydoseofds.com)
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[](https://join.dailydoseofds.com)
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---
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## Contribution
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Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
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