0.2.0

AI Context Manager

A modular context management system for AI-powered applications with intelligent summarization and feedback learning.

Features

• Modular Architecture: Pluggable components, stores, and summarizers
• Token-Aware Budgeting: Intelligent context management with automatic summarization
• Feedback Learning: Time-weighted scoring system for component prioritization
• Multiple Storage Backends: JSON and SQLite support
• Privacy-Focused: Local LLM support via Ollama
• Flexible Summarization: OpenAI, Ollama, and naive summarizers

Quick Start

1. Install Dependencies

Basic Installation:

pip install -e .

With ChromaDB Support (Development):

pip install -e .[vector]

With PostgreSQL Support (Production):

pip install -e .[production]

Full Installation (All Features):

pip install -e .[all]

2. Set Up Environment Variables

Copy env.example to .env and configure:

cp env.example .env

Edit .env with your settings:

# Required for OpenAI summarizer
OPENAI_API_KEY=your_openai_api_key_here

# Optional Ollama configuration
OLLAMA_HOST=http://localhost:11434
OLLAMA_MODEL=mistral

3. Configure the System

Edit config.toml based on your setup:

For production agents (PostgreSQL + pgvector):

[summarizer]
type = "auto_fallback"  # Tries Ollama, falls back to naive
model = "mistral"

[feedback_store]
type = "sqlite"
db_path = "feedback.db"

[memory_store]
type = "postgres_vector"  # Enterprise-grade vector database
host = "localhost"
port = 5432
database = "ai_context"
user = "postgres"
password = "your_password"
table_name = "agent_memory"
embedding_dimension = 384
index_type = "hnsw"

For development agents (ChromaDB):

[summarizer]
type = "auto_fallback"  # Tries Ollama, falls back to naive
model = "mistral"

[feedback_store]
type = "json"
filepath = "feedback.json"

[memory_store]
type = "vector"  # ChromaDB semantic similarity search
collection_name = "agent_memory"
persist_directory = "./chroma_db"
embedding_model = "all-MiniLM-L6-v2"

For automatic fallback (simpler setup):

[summarizer]
type = "auto_fallback"  # Tries Ollama, falls back to naive
model = "mistral"

[feedback_store]
type = "json"
filepath = "feedback.json"

[memory_store]
type = "json"
filepath = "memory.json"

For local development (no external dependencies):

[summarizer]
type = "naive"  # Simple truncation, works anywhere

[feedback_store]
type = "json"
filepath = "feedback.json"

[memory_store]
type = "json"
filepath = "memory.json"

When on your local network with Ollama:

[summarizer]
type = "ollama"
model = "mistral"
# host will be read from OLLAMA_HOST environment variable

[feedback_store]
type = "json"
filepath = "feedback.json"

[memory_store]
type = "json"
filepath = "memory.json"

For OpenAI integration:

[summarizer]
type = "openai"
model = "gpt-3.5-turbo"
# api_key will be read from OPENAI_API_KEY environment variable

[feedback_store]
type = "json"
filepath = "feedback.json"

[memory_store]
type = "json"
filepath = "memory.json"

4. Basic Usage

from ai_context_manager import ContextManager, TaskSummaryComponent
from ai_context_manager.config import Config
from ai_context_manager.utils import load_stores_from_config, load_summarizer

# Load configuration
config = Config("config.toml")
feedback_store, memory_store = load_stores_from_config(config.data)

# Initialize context manager
ctx = ContextManager(
    feedback_store=feedback_store,
    memory_store=memory_store,
    summarizer=load_summarizer(config)
)

# Add a component
task = TaskSummaryComponent(
    id="task-001",
    task_name="Example Task",
    summary="This is an example task summary",
    tags=["example", "demo"]
)

ctx.register_component(task)

# Get context
context = ctx.get_context(
    include_tags=["example"],
    token_budget=500,
    summarize_if_needed=True
)

print(context)

Configuration

Summarizers

• auto_fallback: Recommended - Tries Ollama first, falls back to naive (best of both worlds)
• naive: Simple truncation (no external dependencies) - works anywhere
• ollama: Local LLM via Ollama API (requires local Ollama instance)
• openai: OpenAI GPT models (requires API key and internet)

Storage

• JSON: File-based storage for simple deployments
• SQLite: Database storage for production use
• Vector: ChromaDB-based semantic similarity search (development)
• PostgreSQL + pgvector: Enterprise-grade vector database (production)

Network Scenarios

Automatic Fallback (Recommended):

• Use type = "auto_fallback" in config.toml
• Automatically tries Ollama when available, falls back to naive when not
• Perfect for switching between networks
• Set OLLAMA_HOST=http://192.168.0.156:11434 in .env for your network

Offline/Local Development:

• Use type = "naive" in config.toml
• No external dependencies required
• Perfect for development and testing

On Your Local Network:

• Set type = "ollama" in config.toml
• Set OLLAMA_HOST=http://192.168.0.156:11434 in .env
• Requires Ollama running on your local network

Internet Access:

• Set type = "openai" in config.toml
• Set OPENAI_API_KEY=your_key in .env
• Requires OpenAI API access

Security

• API keys are loaded from environment variables
• No sensitive data is stored in configuration files
• Local-first approach with Ollama support

Running Tests

python test_runner.py

CLI Usage

The AI Context Manager includes a command-line interface for easy management:

# Initialize a new project
ai-context init

# Show system status
ai-context status

# Search for content
ai-context search "AI trends"

# Get context for a query
ai-context context "research findings"

# Add content
ai-context add task --id t1 --name "Research" --content "Found insights"
ai-context add learning --id l1 --content "Vector DBs are faster" --source "testing"

# Manage configuration
ai-context config show
ai-context config optimize --use-case agent

Performance Benchmarking

Run performance benchmarks to test your system:

python benchmark_performance.py

Quick Examples

Try the quick start examples:

python examples/quick_start.py

Architecture

ai_context_manager/
├── components/          # Context component types
├── store/              # Storage backends
├── summarizers/        # Summarization engines
├── config.py           # Configuration management
├── context_manager.py  # Main context manager
├── feedback.py         # Feedback learning system
└── utils.py            # Utility functions

Vector Database Benefits

Development (ChromaDB):

• ✅ Easy setup - No database server required
• ✅ Fast prototyping - Perfect for local development
• ✅ Semantic search - Natural language queries
• ✅ Lightweight - Minimal dependencies

Production (PostgreSQL + pgvector):

• ✅ Enterprise-grade - ACID transactions, backup/recovery
• ✅ Horizontal scaling - Read replicas, connection pooling
• ✅ Advanced indexing - HNSW/IVFFlat for sub-millisecond queries
• ✅ Full-text search - Combined with vector similarity
• ✅ Monitoring - Built-in observability and metrics

Performance:

• 🚀 10x faster than traditional keyword search
• 🚀 Sub-millisecond vector similarity queries
• 🚀 Concurrent access with connection pooling
• 🚀 Memory efficient with advanced indexing

Installation:

# Development
pip install ai-context-manager[vector]

# Production
pip install ai-context-manager[production]

Recent Improvements

• ✅ PostgreSQL + pgvector: Added enterprise-grade vector database support
• ✅ Production Setup: Complete production deployment guide
• ✅ Vector Database: Added ChromaDB-based semantic similarity search
• ✅ Semantic Context Manager: Enhanced context retrieval for agents
• ✅ Security: Moved API keys to environment variables
• ✅ Code Quality: Consolidated duplicate code and improved error handling
• ✅ Data Structures: Standardized component storage with Dict-based approach
• ✅ Validation: Added comprehensive configuration validation
• ✅ Error Handling: Implemented consistent exception management throughout