omni-recall

Omni-Recall: Neural Knowledge & Long-Term Context Engine

Omni-Recall is a high-performance memory management skill designed for AI agents. It enables persistent, cross-session awareness by transforming conversation history and technical insights into high-dimensional vector embeddings, stored in a Supabase (PostgreSQL + pgvector) knowledge cluster with HNSW indexing for fast semantic search.

🚀 Core Capabilities

1. Vector Semantic Search (fetch with query_text): Intelligent natural language queries using vector similarity. Finds semantically related content even with different wording. Returns results ranked by similarity score (0-1). Default threshold: 0.5 (balanced recall and precision).

2. Neural Synchronization (sync): Encodes current session state, user preferences, and operational steps into vectors using the configured LLMHub embedding model. Default is OpenAI's text-embedding-3-small. Includes automatic duplicate detection (skips if cosine similarity > 0.9). Supports optional category and importance fields.

3. Contextual Retrieval (fetch): Pulls historical neural records using natural language queries or time-based filters. Supports similarity threshold tuning (0.5-0.9) and category filtering.

4. User Profile Management (sync-profile / fetch-profile): Manages user roles, preferences, settings, and personas in a dedicated profiles matrix with vector search support.

5. AI Instruction Management (sync-instruction / fetch-instruction): Stores operational requirements for the AI with semantic search capabilities.

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🛠 Usage Examples

Vector Semantic Search (Recommended)

# Search with natural language (default threshold: 0.5)
python scripts/omni_ops.py fetch "如何优化数据库性能" none 10

# Search with custom similarity threshold
python scripts/omni_ops.py fetch "pgvector 索引优化" none 10 none 0.7

# Search last 7 days for AI-related content
python scripts/omni_ops.py fetch "AI Agent 开发" 7 10

# Search instructions with semantic understanding
python scripts/omni_ops.py fetch-instruction "代码风格规范" none 0.5 5

# Search profiles
python scripts/omni_ops.py fetch-profile "用户技能背景" none 0.5 5

# List all records (use 'none' as query)
python scripts/omni_ops.py fetch none 30 10

Similarity Threshold Guide

Threshold	Description	Use Case
0.5	Balanced (Default) ⭐	General search, returns more results
0.6-0.65	Higher precision	More specific matches
0.7-0.8	High precision	Exact matches, very specific queries
0.8+	Very precise	Almost exact matches only

Query Best Practices:

• ✅ Use phrase queries (3-5 words): "pgvector 索引优化", "数据库性能优化"
• ✅ Single-word queries work well with default 0.5: "pgvector", "数据库"
• 📊 Phrase queries typically score 0.60-0.66, single words 0.53-0.57
• 🎯 Default 0.5 provides good balance, returning 3-10 relevant results

Synchronize Session Context

# Basic sync
python scripts/omni_ops.py sync "User is interested in Python optimization." "session-tag" 0.9

# Sync with category and importance
python scripts/omni_ops.py sync "New tech stack insight" "research" 0.9 "technical" 0.8

Synchronize User Profile

# Set a persona
python scripts/omni_ops.py sync-profile "persona" "Experienced Senior Backend Engineer, favors Go and Python."

# Set a preference
python scripts/omni_ops.py sync-profile "preference" "Prefers concise code without excessive comments."

Synchronize AI Instructions

# Set tone
python scripts/omni_ops.py sync-instruction "tone" "Professional yet friendly, use 'Partner' as my nickname."

# Set workflow steps
python scripts/omni_ops.py sync-instruction "workflow" "1. Plan -> 2. Implementation -> 3. Verification -> 4. Summary."

Encrypted Nsfw Memory (Sensitive Context)

# Sync sensitive content (Encrypted at rest + Vector embedding)
python scripts/omni_ops.py sync-nsfw "Sensitive information here" "private-tag" 0.9

# Fetch with semantic search
python scripts/omni_ops.py fetch-nsfw "敏感查询" 30 10 none 0.5

# Fetch full context including nsfw records
python scripts/omni_ops.py fetch-full-context 10 none true

Encrypted Vault (Key-Value Storage)

# Store an encrypted value
python scripts/omni_ops.py sync-vault "ZHIHU_COOKIE" "your_long_cookie_string"

# Fetch and decrypt a value
python scripts/omni_ops.py fetch-vault "ZHIHU_COOKIE"

Batch Synchronize Document/URL

# Sync a markdown file (H1-H5 Splitting)
python scripts/omni_ops.py batch-sync-doc "/path/to/doc.md" "tag" 0.9

# Sync a web page via URL
python scripts/omni_ops.py batch-sync-doc "https://example.com/article" "web-source" 0.9

Fetch Full Context (Recommended for First Recall)

Priority Order: 1. instructions (Persona/Rules) > 2. profiles (User Info/Preferences) > 3. memories (Session History).

# Get ALL instructions + ALL profiles + memories from last 10 days
python scripts/omni_ops.py fetch-full-context 10

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🏗 Schema Setup (Supabase / Postgres)

1. Supabase Knowledge Cluster

Execute the following SQL in your Supabase project to initialize the neural storage layer:

-- Enable the pgvector extension for high-dimensional search
create extension if not exists vector;

-- Create the neural memory matrix with halfvec for efficiency
create table if not exists public.memories (
  id bigint primary key generated always as identity,
  content text not null,          -- Raw neural content
  embedding vector(1536),        -- Neural vector (text-embedding-3-small)
  metadata jsonb,                -- Engine & session metadata
  source text,                   -- Uplink source identifier
  category text default 'general',-- Memory category
  importance real default 0.5,   -- Memory importance weight (0.0-1.0)
  created_at timestamptz default now(),
  updated_at timestamptz default now()
);

-- HNSW index for fast approximate nearest neighbor search (recommended for production)
create index on public.memories using hnsw (embedding vector_cosine_ops) 
  with (m = 16, ef_construction = 64);

-- Create the user profiles matrix
create table if not exists public.profiles (
  id uuid primary key default gen_random_uuid(),
  category text not null,        -- 'role', 'preference', 'setting', 'persona'
  content text not null,         -- Profile description
  embedding vector(1536),       -- Neural vector
  metadata jsonb,               -- Versioning & source
  created_at timestamptz default now(),
  updated_at timestamptz default now()
);

-- HNSW index for profiles
create index on public.profiles using hnsw (embedding vector_cosine_ops)
  with (m = 16, ef_construction = 64);

-- Create the AI instructions matrix
create table if not exists public.instructions (
  id uuid primary key default gen_random_uuid(),
  category text not null,        -- 'tone', 'workflow', 'rule', 'naming'
  content text not null,         -- Instruction detail
  embedding vector(1536),       -- Neural vector
  metadata jsonb,               -- Versioning & source
  created_at timestamptz default now(),
  updated_at timestamptz default now()
);

-- HNSW index for instructions
create index on public.instructions using hnsw (embedding vector_cosine_ops)
  with (m = 16, ef_construction = 64);

-- Create the nsfw matrix (Encrypted Sensitive Memories)
create table if not exists public.nsfw_memories (
  id bigint primary key generated always as identity,
  content text not null,          -- Encrypted neural content (AES-256)
  embedding vector(1536),        -- Neural vector (unencrypted for search)
  source text,                   -- Uplink source identifier
  category text default 'general',-- Memory category
  importance real default 0.5,   -- Memory importance weight (0.0-1.0)
  created_at timestamptz default now(),
  updated_at timestamptz default now()
);

-- HNSW index for nsfw_memories
create index on public.nsfw_memories using hnsw (embedding vector_cosine_ops)
  with (m = 16, ef_construction = 64);

-- Create the encrypted vault table
create table if not exists public.vault (
  key text primary key,          -- Unique variable name
  value text not null,           -- Encrypted content (AES-256)
  updated_at timestamptz default now()
);

2. Environment Configuration

Required variables for the neural uplink:

• LLMHUB_TOKEN: Authorization for the Neural Encoding API (llmhub.ltd)
• LLMHUB_EMBEDDING_MODEL: Optional embedding model name, defaults to text-embedding-3-small
• SUPABASE_PASSWORD: Credentials for the PostgreSQL Knowledge Base

🧠 Engineering Principles

• Dimensionality: 1536-D Vector Space (text-embedding-3-small)
• Indexing: HNSW (Hierarchical Navigable Small World) for production-grade performance
• Search: Cosine similarity with configurable thresholds (default: 0.5)
• Protocol: HTTPS / WebSockets (via Psycopg2)
• Latency: Optimized for real-time sub-second synchronization
• Context Prioritization: instructions > profiles > memories

📚 Documentation

• Quick Start: QUICK_START.md - Get started in 5 minutes
• CLI Usage: CLI_USAGE_EXAMPLES.md - Comprehensive examples
• API Reference: VECTOR_SEARCH_API.md - Complete API docs
• Threshold Guide: DEFAULT_THRESHOLD_RECOMMENDATION.md - Tuning guide

⚠️ Notes

• Ensure psycopg2 and requests are present in the host environment
• Always fetch-full-context at the start of a mission to align with historical objectives
• Perform a sync upon milestone completion to ensure neural persistence
• Use vector search (query_text) for intelligent semantic queries
• Adjust similarity threshold based on your needs (0.6 = balanced, 0.7 = precise, 0.5 = exploratory)