Metadata-Version: 2.4
Name: pdm-memory
Version: 0.1.2
Summary: Pressure-Driven Memory (PDM) — persistent, resonance-based memory for AI apps.
Author: Westfield Innovations LLC
License: MIT License with Patent Scope Clause
        
        Copyright (c) 2026 Westfield Innovations LLC
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        ─────────────────────────────────────────────
        PATENT SCOPE CLAUSE (Additional Terms)
        ─────────────────────────────────────────────
        
        The above MIT license grant covers the software source code in this repository.
        It does not grant any license to patents held or applied for by Westfield
        Innovations LLC, including but not limited to patents covering the following
        methods and systems embodied in the Pressure-Driven Memory (PDM) system:
        
          1. Pressure-based memory retrieval and ranking (threshold-driven recall).
          2. Resonance retrieval via threshold-adjustment search (TAS).
          3. Temporal deformation geometry for deadline-aware memory pressure.
          4. Validation coefficient computation for memory signal quality.
          5. Incremental decay without a scheduler (time-delta-based decay on read).
        
        The license granted by this file permits you to:
          ✓ Use this SDK (pdm-memory) in your applications.
          ✓ Distribute applications that incorporate this SDK.
          ✓ Modify this SDK for your own use or contribution back to this repository.
        
        This license does NOT permit you to:
          ✗ Reimplement the patented PDM algorithms independently (outside this SDK).
          ✗ Use the patent claims of PDM to build a competing product.
          ✗ Sub-license the patent rights granted herein to third parties.
        
        Use of the SDK constitutes acceptance of these additional terms.
        For commercial licensing inquiries beyond the scope described above,
        contact: legal@westfieldinnovations.com
        
Project-URL: Homepage, https://github.com/westfield-innovations/pdm-memory
Project-URL: Bug Tracker, https://github.com/westfield-innovations/pdm-memory/issues
Keywords: ai,memory,llm,rag,pressure-driven,persistent-memory
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
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Requires-Dist: openai>=1.0; extra == "openai"
Requires-Dist: tiktoken>=0.7; extra == "openai"
Provides-Extra: anthropic
Requires-Dist: anthropic>=0.25; extra == "anthropic"
Provides-Extra: gemini
Requires-Dist: google-genai>=0.1.0; extra == "gemini"
Provides-Extra: ollama
Requires-Dist: ollama>=0.2.0; extra == "ollama"
Provides-Extra: groq
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Provides-Extra: all
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Requires-Dist: tiktoken>=0.7; extra == "all"
Requires-Dist: anthropic>=0.25; extra == "all"
Requires-Dist: google-genai>=0.1.0; extra == "all"
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Provides-Extra: dev
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Requires-Dist: openai>=1.0; extra == "dev"
Requires-Dist: anthropic>=0.25; extra == "dev"
Requires-Dist: tiktoken>=0.7; extra == "dev"
Requires-Dist: google-genai>=0.1.0; extra == "dev"
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Requires-Dist: groq>=0.9.0; extra == "dev"
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Dynamic: license-file

# PDM — Memory for AI Apps That Works Like Memory

[![PyPI](https://img.shields.io/pypi/v/pdm-memory)](https://pypi.org/project/pdm-memory/)
[![Python](https://img.shields.io/pypi/pyversions/pdm-memory)](https://pypi.org/project/pdm-memory/)
[![CI](https://github.com/westfield-innovations/pdm-memory/actions/workflows/ci.yml/badge.svg)](https://github.com/westfield-innovations/pdm-memory/actions)
[![License: MIT](https://img.shields.io/badge/License-MIT%2BPatent-blue)](LICENSE)

Your LLM forgets everything between conversations. The standard fix — stuff a vector database into the context window — is expensive, slow, and retrieves **what matches words, not what matters**.

**PDM stores meaning signatures instead of raw text.** Memories that get used grow stronger. Memories that don't, fade. Retrieval works by resonance: the question itself surfaces what's relevant, instead of a keyword search digging for it.

- 🔑 **Your API key.** Works with your existing Anthropic/OpenAI account.
- 🗄️ **Your storage.** One local file. Your data never leaves your machine. Check the source — there's no phone-home in it.
- ⚡ **Ten minutes.** `pip install pdm-memory` → three lines → persistent memory.

**Benchmarks vs standard RAG:** [link] · Run yourself: `python -m pdm_memory.bench`

---

## Table of Contents

1. [Privacy Mode](#-privacy-mode-local-sqlite)
2. [Ecosystem Mode](#-ecosystem-mode-azus-cloud)
3. [LLM Adapters](#-llm-adapters)
4. [Data Ingestion](#-data-ingestion)
5. [Developer Tools](#-developer-tools)
6. [API Reference](#-api-reference)
7. [License](#-license)

---

## 🔒 Privacy Mode (Local SQLite)

Zero setup. No network calls. Your data stays in a single file on your machine.

### Install

```bash
pip install pdm-memory
# With OpenAI support:
pip install "pdm-memory[openai]"
# With Anthropic support:
pip install "pdm-memory[anthropic]"
# Everything:
pip install "pdm-memory[all]"
```

### Quick Start

```python
from pdm_memory import Memory

# One line to start. The .db file is created automatically.
mem = Memory(store="./my_app_memory.db")

# Write: PDM assigns pressure and stores a signature.
mem.save("User prefers metric units and short answers", source="chat",
         tags=["units", "formatting", "preferences"])

# Read: resonance retrieval — surfaces what's relevant, not just what matches.
hits = mem.recall("how should I format the answer?", k=5)

for h in hits:
    print(h.text, h.pressure, h.last_reinforced)

# Reinforce a memory manually (recall() does this automatically).
mem.reinforce(hits[0].id)

# Inspect why a memory surfaced.
report = mem.explain(hits[0].id, query="how should I format the answer?")
print(report.render())

# Decay runs automatically on each recall(). Manual trigger:
counts = mem.decay()
print(f"Decayed: {counts['decayed']}, Deleted: {counts['deleted']}")
```

### Privacy-First Mode

Store only SHA-256 hashes of memory text — the content never touches disk:

```python
mem = Memory(store="./private.db", store_raw=False)
```

---

## ☁️ Ecosystem Mode (AZUS Cloud)

Connect to the AZUS Companion API to sync memories across devices and share them with the AI companion.

### Connect to the Cloud

```python
from pdm_memory import Memory

mem = Memory(
    store="cloud",
    token="eyJ...",               # Your AZUS JWT access token
    cloud_url="https://api.azus.ai",
)

# All save/recall operations go to the cloud.
mem.save("User's team is in Kyiv (UTC+3)", tags=["location", "team", "timezone"])
hits = mem.recall("what timezone are they in?")
```

### Sync Local ↔ Cloud

```python
# Start with a local store
local_mem = Memory(store="./local.db")
local_mem.save("Local preference", tags=["pref", "local", "test"])

# Push local memories to cloud
report = local_mem.sync(
    direction="push",
    token="eyJ...",
    cloud_url="https://api.azus.ai",
)
print(report)   # SyncReport(pushed=1, pulled=0, conflicts=0, errors=0)

# Pull cloud memories to local
report = local_mem.sync(direction="pull", token="eyJ...")

# Two-way sync (higher pressure wins on conflict)
report = local_mem.sync(direction="bidirectional", token="eyJ...")
```

### JWT Token Handling

```python
from pdm_memory.auth import JWTAuth

# Tokens are refreshed automatically when they expire
auth = JWTAuth(
    token="eyJ...",
    refresh_token="eyJ...",
    refresh_url="https://api.azus.ai/api/v1/accounts/auth/refresh/",
)
```

---

## 🤖 LLM Adapters

The wrapper is the demo; the primitives are the product. Most developers start here.

### OpenAI

```python
from pdm_memory import Memory
from pdm_memory.integrations import wrap_openai

mem = Memory(store="./my_app.db")
client = wrap_openai(api_key="sk-...", memory=mem)

# Memory is handled completely invisibly:
# - Before the call: relevant memories are injected into the system prompt
# - After the call: user message + AI reply are saved to memory
reply = client.chat("What units should I use?")
print(reply)
```

### Anthropic

```python
from pdm_memory.integrations import wrap_anthropic

client = wrap_anthropic(api_key="sk-ant-...", memory=mem)
reply = client.chat("What units should I use?")
```

### Manual Control

```python
from pdm_memory.integrations import ContextWindowManager

# Control exactly what goes into context
manager = ContextWindowManager(max_tokens=1500, model="gpt-4o")
hits = mem.recall("user's formatting preferences", k=10)
trimmed = manager.fit(hits)                    # Drop lowest-pressure memories first
system_block = manager.format_for_prompt(trimmed)
print(system_block)
```

---

## 📥 Data Ingestion

### Import Legacy Data

```python
# From a list of dicts
mem.ingest(
    data_source=[
        {"text": "User hates Comic Sans", "importance": 85},
        {"content": "Team deploys on Fridays — bad idea", "labels": "devops,process,risk"},
    ],
    mapping={"text": "compressed_fact", "importance": "p_magnitude"},
)

# From a CSV file (auto-detects common column names)
mem.ingest("./old_chat_logs.csv")

# With progress tracking
def on_progress(processed, total):
    print(f"{processed}/{total} records processed")

mem.ingest("./large_dataset.csv", on_progress=on_progress)
```

### Auto-Generate Signatures with an LLM

```python
import openai
client = openai.OpenAI(api_key="sk-...")

# LLM will compress raw text → compressed_fact + 3 tags + p_magnitude
mem.ingest(
    data_source=["User complains about slow API responses every Monday morning"],
    llm_client=client,
)
```

### Batch Processing (Large Datasets)

```python
# 10,000 records processed in batches of 50, with rate limiting
mem.ingest(
    data_source="./10k_records.csv",
    batch_size=50,
)
```

---

## 🛠️ Developer Tools

### The explain Method

```python
report = mem.explain(memory_id, query="how should I format this?")
print(report.render())
```

```
╔══════════════════════════════════════════════════════
║  PDM Memory Explain Report
╠══════════════════════════════════════════════════════
║  ID:              abc12345-...
║  Fact:            User prefers metric units and short answers
║  Tags:            units, formatting, preferences
╠──────────────────────────────────────────────────────
║  Pressure Components:
║    p_magnitude:    80.00
║    V coefficient:  0.8333  (4 retrievals)
║    Decay factor:   0.0231  (1.0d since retrieved, T½=30d)
║    Intent weight:  1.0000
║    Quality:        0.80
║    ─────────────────────────────
║    P_effective:    55.28
╠──────────────────────────────────────────────────────
║  Resonance (TAS coupling):
║    coupling_score:     0.8750
║    tag_overlap:        1.0000
║    domain_match:       1.0000
╚══════════════════════════════════════════════════════
```

### Benchmark Harness

```bash
# Run full benchmark (PDM vs keyword+recency baseline)
python -m pdm_memory.bench

# Quick smoke test (5 scenarios)
python -m pdm_memory.bench --quick

# Save results as JSON
python -m pdm_memory.bench --output results.json
```

### CLI Tool

```bash
# List all memories
pdm-cli list-memories --store ./my_app.db

# Filter by pressure
pdm-cli list-memories --store ./my_app.db --min-pressure 60

# Explain a specific memory
pdm-cli explain abc12345 --store ./my_app.db --query "formatting"

# Trigger a decay pass (dry run first)
pdm-cli decay --store ./my_app.db --dry-run
pdm-cli decay --store ./my_app.db

# Show stats
pdm-cli stats --store ./my_app.db

# List drawers (categories)
pdm-cli drawers --store ./my_app.db

# Sync to cloud
pdm-cli sync --store ./my_app.db --token eyJ... --direction push
```

---

## 📖 API Reference

### `Memory(store, user, token, cloud_url, store_raw)`

| Method | Description |
|--------|-------------|
| `save(text, source, tags, p_magnitude, t_persistence, drawer, regime, deadline)` | Store a new memory |
| `recall(query, k, min_pressure, search_cost, drawer, reinforce)` → `List[MemoryHit]` | Retrieve top-k relevant memories |
| `reinforce(memory_id, coupling_score)` | Manually raise a memory's pressure |
| `decay(dry_run)` → `dict` | Trigger decay pass (runs automatically on recall) |
| `explain(memory_id, query)` → `ExplainReport` | Show why a memory has its current pressure |
| `sync(direction, token, cloud_url)` → `SyncReport` | Sync local ↔ cloud |
| `ingest(data_source, mapping, llm_client, batch_size)` → `dict` | Import legacy data |
| `list_drawers()` → `List[DrawerInfo]` | List memory categories |
| `count()` → `int` | Total memory count |
| `close()` | Release storage connections |

### `MemoryHit`

| Field | Description |
|-------|-------------|
| `id` | UUID |
| `text` | Memory content |
| `pressure` | Live P_effective at retrieval time |
| `p_raw` | Stored p_magnitude |
| `intent_tags` | Classification tags |
| `coupling_score` | TAS resonance score (0–1) |
| `last_reinforced` | Last retrieval datetime |

---

## 🔬 How PDM Works

**Pressure** — every memory has a p_magnitude (0–100). Important, frequently-used memories stay strong. Unused ones decay. You control the baseline; the system adjusts dynamically.

**Decay** — computed at recall time based on elapsed days vs. domain-specific half-lives. No scheduler required (Celery-free). Market signals decay in 1 day; core facts persist for a year.

**Retrieval (TAS)** — Threshold-Adjustment Search lowers the pressure threshold based on query uncertainty (search_cost). Then coupling scores rank memories by tag overlap, domain, regime, and pressure proximity. The most resonant memories surface first.

**Validation Coefficient (V)** — Laplace-smoothed accuracy tracker. Memories that prove predictively useful grow stronger; ones that mislead decay faster.

---

## 🏗️ Custom Storage Backend

Implement `BaseStorage` to add your own backend (Postgres, Redis, DynamoDB…):

```python
from pdm_memory.storage.base import BaseStorage

class MyPostgresStorage(BaseStorage):
    def save(self, sig): ...
    def get(self, memory_id, user): ...
    def update(self, memory_id, **fields): ...
    def delete(self, memory_id, user): ...
    def list(self, user, limit, min_pressure, drawer): ...
    def list_drawers(self, user): ...

mem = Memory.__new__(Memory)
mem._storage = MyPostgresStorage(...)
mem._user = "alice"
mem._engine = RetrievalEngine()
```

---

## 📄 License

[MIT + Patent Scope Clause](LICENSE) — use the SDK freely; the PDM algorithm patents stay with Westfield Innovations LLC.

Built by **Westfield Innovations LLC** · [westfieldinnovations.com](https://westfieldinnovations.com)
