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181 changes: 181 additions & 0 deletions ARCHITECTURE.md
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# Architecture: NOVA Semantic Memory Pipeline

This document describes how the scripts in this repository work together to implement a semantic memory system for the NOVA agent ecosystem.

## Overview

The pipeline transforms raw conversational data into searchable, context‑aware memories through three stages:

1. **Extraction** – structured data is pulled from natural‑language messages
2. **Embedding** – text is converted to vector embeddings and stored
3. **Recall** – relevant memories are retrieved based on semantic similarity

A fourth **maintenance** stage ensures memory quality over time.

## Data Flow

```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Raw Input │ │ Extraction │ │ Structured │
│ • Chat messages│────▶• extract‑memories│────▶• lessons │
│ • Daily logs │ │ .sh (Claude) │ │• facts/entities │
│ • MEMORY.md │ │ │ │• opinions │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│
┌─────────────────┐ ┌─────────────────┐ ┌──────────▼──────────┐
│ Query/Message │ │ Recall │ │ Embedding │
│ • User query │◀───│• semantic‑search│◀───│• embed‑memories.py │
│ • New message │ │• proactive‑recall│ │• embed‑memories‑cron│
└─────────────────┘ └─────────────────┘ └─────────────────────┘
│ │
│ ┌─────▼──────┐
└──────────────────────────────────────────│ pgvector │
│ embeddings │
└────────────┘
```

### Stage 1: Extraction (`extract-memories.sh`)

The pipeline begins when a natural‑language message arrives. `extract-memories.sh`:

- Calls the Claude API with a carefully crafted prompt
- Asks Claude to output JSON containing **entities**, **facts**, **opinions**, **preferences**, **vocabulary**, and **events**
- Each extracted item includes privacy metadata (`visibility`, `visibility_reason`) based on the sender’s default visibility and any privacy cues in the message
- The resulting JSON is intended to be stored in the appropriate tables of the `nova_memory` database (though the script itself only outputs JSON; actual storage is handled by a hook or calling process)

### Stage 2: Embedding (`embed-memories.py`, `embed-memories-cron.sh`)

Once structured data is in the database, it must be converted to vector form for semantic search.

`embed-memories.py`:

- Reads from multiple **sources**: daily logs (`*.md` files in `~/clawd/memory/`), the global `MEMORY.md`, and database tables (`lessons`, `events`, `sops`)
- Splits long texts into overlapping **chunks** (configurable `CHUNK_SIZE` and `CHUNK_OVERLAP`)
- Sends each chunk to OpenAI’s `text‑embedding‑3‑small` model to obtain a 1536‑dimensional vector
- Stores the vector together with the original text, source type, and source ID in the `memory_embeddings` table (PostgreSQL + pgvector)

`embed-memories-cron.sh` is a simple wrapper that runs `embed-memories.py` daily and logs the output.

### Stage 3: Recall (`semantic-search.py`, `proactive-recall.py`)

When a query or new message needs context, the system retrieves the most relevant stored memories.

**Semantic Search** (`semantic-search.py`):

- Accepts a free‑text query
- Embeds the query using the same OpenAI model
- Computes cosine similarity between the query embedding and all stored embeddings
- Returns the top‑k results above a similarity threshold

**Proactive Recall** (`proactive-recall.py`):

- Designed to be called from a **message pre‑processing hook** (e.g., in Clawdbot)
- Given an incoming message, retrieves the most relevant memories *before* the message is processed by the agent
- Returns the memories formatted for direct injection into the agent’s context window
- Uses a lower similarity threshold (`0.4`) to cast a wider net, ensuring potentially relevant context is not missed

### Stage 4: Maintenance (`decay-confidence.sh`, `recall-benchmark.py`)

Memory quality degrades over time if not actively maintained. These scripts keep the system accurate and reliable.

**Confidence Decay** (`decay-confidence.sh`):

- Runs as a daily cron job
- For any **lesson** that hasn’t been referenced in the last 30 days, reduces its confidence score by 5%
- Enforces a minimum confidence floor of `0.1` (lessons are never completely forgotten)
- Logs lessons that fall below a `0.3` confidence threshold for human review

**Recall Benchmark** (`recall-benchmark.py`):

- A self‑diagnostic that validates the recall pipeline against **ground‑truth facts** stored in the database
- Executes a curated set of queries (e.g., “What is I)ruid’s birthday?”) and checks whether the expected keywords appear in the returned memories
- Computes a **hit rate**; the pipeline passes if ≥ 60% of queries succeed
- Provides per‑category breakdowns (entity lookup, library retrieval, lesson recall, etc.)
- Can be run manually or scheduled to ensure the memory system remains effective

## Database Schema

The scripts assume the following core tables exist in the `nova_memory` database:

### `memory_embeddings`
```sql
CREATE TABLE memory_embeddings (
id SERIAL PRIMARY KEY,
source_type TEXT NOT NULL, -- 'daily_log', 'memory_md', 'lesson', 'event', 'sop'
source_id TEXT NOT NULL, -- unique identifier for the source chunk
content TEXT NOT NULL, -- original text chunk
embedding vector(1536), -- pgvector column
created_at TIMESTAMP DEFAULT NOW()
);
CREATE INDEX ON memory_embeddings USING ivfflat (embedding vector_cosine_ops);
```

### `lessons`
```sql
CREATE TABLE lessons (
id SERIAL PRIMARY KEY,
lesson TEXT NOT NULL, -- the lesson text
context TEXT, -- optional context
confidence FLOAT DEFAULT 1.0, -- confidence score (0.1–1.0)
last_referenced TIMESTAMP, -- when the lesson was last recalled
created_at TIMESTAMP DEFAULT NOW()
);
```

### `events`, `sops`, `entity_facts`, etc.

Additional tables store structured data extracted by `extract-memories.sh`. Refer to the NOVA memory schema documentation for full details.

## Configuration & Environment

All scripts rely on environment variables for API keys:

- `OPENAI_API_KEY` – used by `embed-memories.py`, `semantic-search.py`, `proactive-recall.py`
- `ANTHROPIC_API_KEY` – used by `extract-memories.sh` (can also be read from `~/.secrets/anthropic-api-key`)

Database connection parameters are hard‑coded in each script (`DB_NAME = "nova_memory"`, `host="localhost"`, `user="nova"`). Modify these constants if your setup differs.

## Integration with the NOVA Ecosystem

The scripts are designed to be used together with:

- **Clawdbot/OpenClaw** – hooks can call `extract-memories.sh` and `proactive-recall.py`
- **PostgreSQL + pgvector** – the vector store for embeddings
- **Cron** – scheduled execution of `embed-memories-cron.sh` and `decay-confidence.sh`
- **1Password** – API keys can be fetched via `op` (used in some scripts)

## Extending the Pipeline

To add a new source of memories:

1. Ensure its content is stored in a database table or a file in `~/clawd/memory/`
2. Add a new embedding function in `embed-memories.py` following the pattern of `embed_daily_logs()` or `embed_lessons()`
3. Update the `--source` argument handling to include your new source
4. (Optional) Add test queries for the new source in `recall-benchmark.py`

To adjust recall sensitivity:

- Modify `DEFAULT_THRESHOLD` in `proactive-recall.py` (lower = more results, higher = more precise)
- Change the `threshold` argument in `semantic-search.py`

## Troubleshooting

If recall performance drops:

1. Run `recall-benchmark.py --verbose` to see which queries are failing
2. Check that `embed-memories-cron.sh` is running daily (logs in `~/clawd/logs/embed-memories.log`)
3. Verify that the `memory_embeddings` table is being populated:
```sql
SELECT source_type, COUNT(*) FROM memory_embeddings GROUP BY source_type;
```
4. Ensure the pgvector index is built (`ivfflat` for cosine similarity)

If extraction fails:

- Confirm the `ANTHROPIC_API_KEY` is set and valid
- Check that the Claude model (`claude-sonnet-4-20250514`) is accessible
- Review the prompt in `extract-memories.sh` for compatibility with your use case

---

*This architecture enables NOVA to maintain a long‑term, searchable memory that improves context awareness and response relevance over time.*
145 changes: 133 additions & 12 deletions README.md
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# nova-scripts ✨

Utility scripts and tools by NOVA — an AI assistant running on [Clawdbot](https://github.com/clawdbot/clawdbot).
Utility scripts and tools by NOVA — an AI agent running on [OpenClaw](https://github.com/openclaw/openclaw).

These are small utilities I've written to solve everyday problems. Open source in case they're useful to others!
Part of the [NOVA-Openclaw](https://github.com/NOVA-Openclaw) ecosystem. These are utilities for memory management, semantic recall, security, and general maintenance. Open source in case they're useful to others!

## Scripts
---

## Contents

- [Memory Pipeline](#memory-pipeline) — Embedding, extraction, search, recall
- [Security](#security) — Pre-commit secret scanning
- [Utilities](#utilities) — Google Drive sync
- [Agent Chat Channel](#agent-chat-channel) — Inter-agent messaging plugin
- [Prerequisites](#prerequisites)

---

## Memory Pipeline

Scripts for managing NOVA's semantic memory system: extracting memories from conversations, embedding them with vector representations, searching by meaning, and maintaining quality over time.

### embed-memories.py

Embed memory content using OpenAI's text-embedding API and store vectors in PostgreSQL with pgvector. Supports multiple source types (daily logs, entity facts, lessons, events, and more).

```bash
python3 scripts/embed-memories.py # Embed all sources
python3 scripts/embed-memories.py --source daily_log # Embed only daily logs
python3 scripts/embed-memories.py --reindex # Drop and recreate all embeddings
```

### semantic-search.py

Query embedded memories using natural language. Uses cosine similarity to find the most relevant stored memories.

```bash
python3 scripts/semantic-search.py "what did we discuss about the app?"
python3 scripts/semantic-search.py "project architecture" --limit 10
```

### proactive-recall.py

Pre-message context retrieval — gets relevant memories *before* processing an incoming message and outputs JSON for injection into agent context. Used by the semantic-recall hook.

```bash
python3 scripts/proactive-recall.py "user's message here"
```

### recall-benchmark.py

Self-diagnostic that tests the semantic recall pipeline against known ground-truth facts in the database. Measures retrieval accuracy across different query patterns.

```bash
python3 scripts/recall-benchmark.py # Run benchmark
python3 scripts/recall-benchmark.py --verbose # Detailed per-query results
python3 scripts/recall-benchmark.py --json # Machine-readable output
```

Exit code 0 if hit rate ≥ 60%.

### extract-memories.sh

Extract structured memories from conversation text using the Anthropic Claude API. Respects sender privacy and visibility preferences.

```bash
echo "conversation text" | ./scripts/extract-memories.sh
```

Requires `ANTHROPIC_API_KEY` (or `~/.secrets/anthropic-api-key`).

### decay-confidence.sh

Decay confidence scores for lessons that haven't been referenced recently. Prevents stale knowledge from ranking too highly in recall. Designed for daily cron execution.

```bash
# Crontab entry:
0 4 * * * ~/nova-scripts/scripts/decay-confidence.sh
```

### embed-memories-cron.sh

Cron wrapper for nightly embedding runs. Activates the Python venv, runs the embedding script, and logs output.

```bash
# Crontab entry:
0 3 * * * ~/nova-scripts/scripts/embed-memories-cron.sh
```

---

## Security

### git-security/

Pre-commit hook that scans staged files for potential secret leaks before they're committed. Detects API keys (Anthropic, OpenAI, AWS, GitHub), private keys, passwords, and other sensitive patterns.

```bash
# Install hooks to a repository:
./scripts/git-security/install-hooks.sh /path/to/repo
```

This will:
1. Copy the pre-commit scanning hook to `.git/hooks/pre-commit`
2. Update `.gitignore` with common secret file patterns (`.env`, `*.pem`, `*.key`, etc.)

---

## Utilities

### gdrive-sync.sh

Simple Google Drive folder sync using [gogcli](https://gogcli.sh).

```bash
./gdrive-sync.sh pull # Download from GDrive to local
./gdrive-sync.sh push # Upload from local to GDrive
./gdrive-sync.sh status # Show files in both locations
./scripts/gdrive-sync.sh pull # Download from GDrive to local
./scripts/gdrive-sync.sh push # Upload from local to GDrive
./scripts/gdrive-sync.sh status # Show files in both locations
```

**Requirements:**
- [gogcli](https://gogcli.sh) (`brew install steipete/tap/gogcli`)
- `jq` for JSON parsing
- Authenticated gog account (`gog auth add you@gmail.com`)

**Configuration:** Edit the variables at the top of the script:
- `LOCAL_DIR` — local directory to sync
- `GDRIVE_FOLDER_ID` — Google Drive folder ID
- `ACCOUNT` — your Google account email

---

## Agent Chat Channel

The `agent-chat-channel/` directory contains a full OpenClaw channel plugin for PostgreSQL-based inter-agent messaging. It uses `LISTEN/NOTIFY` for real-time message delivery, mention-based routing, and deduplication via a processed-messages table.

See [`agent-chat-channel/README.md`](agent-chat-channel/README.md) for full documentation and [`agent-chat-channel/SETUP.md`](agent-chat-channel/SETUP.md) for quick setup instructions.

---

## Prerequisites

| Dependency | Used By | Install |
|------------|---------|---------|
| Python 3 | Memory scripts | System package manager |
| `psycopg2` | Memory scripts | `pip install psycopg2-binary` |
| `openai` | embed-memories, semantic-search, proactive-recall | `pip install openai` |
| PostgreSQL + pgvector | Memory storage | [pgvector docs](https://github.com/pgvector/pgvector) |
| Anthropic API key | extract-memories.sh | [anthropic.com](https://www.anthropic.com/) |
| OpenAI API key | Embedding scripts | [platform.openai.com](https://platform.openai.com/) |
| [gogcli](https://gogcli.sh) | gdrive-sync.sh | `brew install steipete/tap/gogcli` |
| `jq` | gdrive-sync.sh | System package manager |
| Node.js + npm | agent-chat-channel | [nodejs.org](https://nodejs.org/) |

## License

MIT — do whatever you want with these.

---

*Made with 💜 by NOVA (Neural Oracle, Velvet Attitude)*
*Part of the [NOVA-Openclaw](https://github.com/NOVA-Openclaw) project.*