CPPTAI

Python framework (standard library only) for a 5‑phase cognitive pipeline, DeepSeek API integration (OpenAI‑compatible), automated benchmarks with CSV/JSON reports, and LaTeX research generation with auto‑loaded tables and plots.

Overview

• Five‑phase architecture: Entropic segregation → Vertical topology → Cognitive descent → External convergence → Presentation (Phase V).
• Minimal DeepSeek client with .env key management and model fallback.
• Benchmarks: accuracy vs baselines (CoT, ToT, GoT, ReAct), diversity (Shannon on clusters), error rates (GSM8K/MATH/AIME), time‑per‑problem; outputs in benchmarks.csv and benchmarks.json.
• LaTeX research (research.tex) using pgfplotstable/pgfplots to load results directly.

Requirements

• Python ≥ 3.10.
• No external dependencies; everything uses the standard library.
• Optional: DeepSeek API key for live responses.

Setup

1. Create a .env file at the project root:

   DEEPSEEK_API_KEY=your_key
   

   Do not share or commit real keys.

2. Main file structure:

   • src/cpptai/core.py – 5‑phase orchestrator.
   • src/cpptai/deepseek_client.py – DeepSeek API client with .env loader.
   • src/cpptai/benchmarks.py – execution and metrics, CSV/JSON outputs.
   • src/cpptai/presentation.py – Phase V formatting (executive/technical/public).
   • src/cpptai/types.py – base types (difficulty, problem blocks).
   • src/cpptai/env.py – .env loader without dependencies.
   • src/main.py – startup CLI.
   • tests/ – unit tests.
   • research.tex – LaTeX document (auto‑loaded charts/tables).

Quick Start

• Run the main pipeline:

  • Windows PowerShell: python .\src\main.py
  • Alternative: python -m src.main

• Primary outputs:

  • ragionamenti.csv – pipeline artifacts.
  • memoria.json – execution state/memory.
  • benchmarks.csv and benchmarks.json – quantitative results.

Benchmarks

• Implemented metrics:
  • Accuracy vs baselines: CoT, ToT, GoT, ReAct.
  • Diversity: Shannon entropy over solution clusters (hash embeddings + k‑means).
  • Error rates: GSM8K/MATH/AIME sets (proxy/placeholder if data unavailable).
  • Average time per problem and token counts.
• Execution: run src/main.py; benchmarks run automatically and are saved to benchmarks.csv/benchmarks.json.

LaTeX Research

• research.tex loads benchmarks.csv with pgfplotstable and generates tables/plots.
• Typical compilation:
  • pdflatex research.tex
  • Repeat compilation if needed to update references.
• Requires a LaTeX distribution with pgfplots/pgfplotstable (e.g., TeX Live/MiKTeX).

Tests

• Run unit tests:
  • python -m unittest discover -s tests -p "test_*.py" -q
• Tests use only the standard library’s unittest.

Security Notes

• Do not commit API keys/secrets.
• The client avoids logging sensitive content and uses fallbacks when no key is present.

Troubleshooting

• Import errors in tests: ensure src is on PYTHONPATH or use the commands above; tests already include a local path fix.
• No response from DeepSeek: verify DEEPSEEK_API_KEY in .env and connectivity.

Availability

• Repository: https://github.com/fra150/CPPTAI

Generate Benchmarks and Figures

• Disable external calls if needed: PowerShell $env:BENCH_DISABLE_EXTERNAL=1; python .\src\main.py
• Outputs:
  • benchmarks.csv, benchmarks_summary.csv, benchmarks.json
  • cumulative_accuracy.csv, error_by_phase.csv, stats_summary.csv
• Compile LaTeX: pdflatex research.tex (twice for references).

License

• MIT open‑source