Metadata-Version: 2.4
Name: ain-research
Version: 1.0.1
Summary: Autonomous Intelligence Network (AIN) - Core Research & Knowledge Compiler
Home-page: https://github.com/That-Tech-Geek/ain-research
Author: Sambit Mishra
Author-email: sambit1912@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: requests
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# AIN Research

> **Autonomous Intelligence Network — Core Research & Knowledge Compilation Engine**

[![Python](https://img.shields.io/badge/python-3.8%2B-blue)](https://python.org)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![PyPI](https://img.shields.io/pypi/v/ain-research.svg)](https://pypi.org/project/ain-research/)

`ain-research` is the core orchestration engine powering the **Autonomous Intelligence Network (AIN)** — a self-organizing research system that autonomously ingests scientific literature, detects contradictions between knowledge nodes, and evolves credibility scores for every research concept it tracks.

---

## 🚀 Quick Start & Installation

```bash
pip install ain-research
```

**Verify the installation:**
```python
import ain_research
```

> [!IMPORTANT]
> **AIN High-Fidelity Standard**: As of v1.0.0, the Autonomous Intelligence Network (AIN) has autonomously enforced rigorous Google Style docstrings and exhaustive static typing across all modules (`ain`, `db_manager`, `contradiction_engine`, `credibility_manager`, `infinite_research_daemon`).

---

## 🔬 Core Architecture & API Reference

### 1. Knowledge Orchestrator (`ain`)

The central brain routes new research concepts to the correct vault subdirectory, compiles the knowledge wiki incrementally, and drives all downstream intelligence modules.

**Usage:**
```python
import argparse
from ain_research.ain import cmd_remember, compile_wiki

# Save a concept (auto-routes based on tags)
args = argparse.Namespace(
    title="Hawkes Process in Market Making",
    content="A self-exciting point process where each trade increases P(next trade)...",
    tags="quant,finance,microstructure"
)
cmd_remember(args)
# → Saved to: vault/wiki/02_Research/Quant_Finance/

# Rebuild the full knowledge index
compile_wiki()
```

### 2. Autonomous Research Daemon (`infinite_research_daemon`)

Crawls ArXiv and GitHub continuously, maintaining stateful pagination and rate-limit-aware exponential backoff.

**Usage:**
```bash
# Single pass
python -m ain_research.infinite_research_daemon --run-once

# Ingest a specific paper
python -m ain_research.infinite_research_daemon --paper 2406.12345
```

### 3. Storage Foundation (`db_manager`)

OS-level atomic file locking + SQLite backing store with dead-letter queue logic.

**Usage:**
```python
from ain_research.db_manager import FileLock, get_system_metrics

with FileLock():
    # safe critical section
    pass

metrics = get_system_metrics()
print(f"Queue pending: {metrics['queue_pending']}")
```

### 4. Contradiction Engine (`contradiction_engine`)

Zero-LLM-token 3-voter ensemble that detects mutually inconsistent research claims in < 500ms on 19,000+ nodes.

**Voters:**
- TF-IDF cosine similarity (≥ 0.72)
- Jaccard unigram overlap (≥ 0.45)
- High semantic sim + tag disjointness

**Usage:**
```python
from ain_research.contradiction_engine import detect_contradictions, get_unresolved_count

# Preview conflicts without writing to disk
conflicts = detect_contradictions(all_pages, dry_run=True)
print(f"Unresolved: {get_unresolved_count()}")
```

### 5. Credibility Manager (`credibility_manager`)

Self-evolving node reputation scoring with automatic archival and first-principles promotion lifecycle.

```
confirmed → score += 0.1  (max 1.0)
falsified → score -= 0.2  (min 0.0)
score < 0.3 → archived (_Archived_Falsified/)
score > 0.8 for 30d → first_principles (_First_Principles/)
```

**Usage:**
```python
from ain_research.credibility_manager import record_confirmation, get_all_scores_summary

record_confirmation("Hawkes_Process_in_Market_Making")
for r in get_all_scores_summary()[:5]:
    print(f"{r['score']:.2f} [{r['status']}] {r['slug']}")
```

---

## 🏛️ System Architecture

```
ArXiv / GitHub API
        ↓
infinite_research_daemon  →  db_manager (SQLite Queue)
                                        ↓
                               ain.py Orchestrator
                               ↙        ↓        ↘
                    vault .md    contradiction   credibility
                    INDEX.md     engine          manager
                    MOCs         _Disputes/      _First_Principles/
                    visualizer_data.json
```

---

## 🤝 Contribution & Links

- **Repository**: [https://github.com/That-Tech-Geek/ain-research](https://github.com/That-Tech-Geek/ain-research)
- **PyPI**: [https://pypi.org/project/ain-research/](https://pypi.org/project/ain-research/)
- **Author**: Sambit Mishra

## License

[MIT](LICENSE) © 2026 Sambit Mishra / AIN Labs
