qv-session-manager 0.3.0

Simple, efficient session manager for persistent storage of conversations from qv-ollama-sdk in SQLite

QV Session Manager

A simple, efficient session manager for persistent storage and management of conversations from qv-ollama-sdk in an SQLite database.

✨ Features

• 💾 Persistent storage of conversations and messages in SQLite
• 🔍 Full-text search in conversation contents and titles
• ⏰ Time-based search by creation/update date
• 🔄 Conversation resumption at any point
• 📋 Conversation management (list, delete, load)
• 🎯 Minimal dependencies (only Python stdlib + qv-ollama-sdk)
• 🚀 Simple API with clear, intuitive methods

🛠️ Installation

From PyPI

pip install qv-session-manager

or

uv add qv-session-manager

Development installation

git clone https://github.com/quantyverse/qv-session-manager.git
cd qv-session-manager
pip install -e .

Dependencies

• Python 3.10+
• qv-ollama-sdk

🚀 Quickstart

from qv_session_manager import SessionManager
from qv_ollama_sdk.domain.models import Conversation

# Initialize SessionManager
mgr = SessionManager(db_path="my_sessions.db")

# Create new conversation
conv = Conversation(title="Python Help", model_name="llama3")
conv.add_user_message("Explain Python lists to me!")
conv.add_assistant_message("Lists are ordered, mutable collections...")

# Save
mgr.save_conversation(conv, conv.messages)

# Load
loaded = mgr.load_conversation(str(conv.id))
print(f"Loaded: {loaded.title} with {len(loaded.messages)} messages")

# Search
results = mgr.search_conversations("lists")
print(f"Found: {len(results)} conversations")

📚 API Documentation

SessionManager

Initialization

SessionManager(db_path: str = "session_manager.db")

• db_path: Path to the SQLite database file

Methods

save_conversation(conversation, messages)

Saves a conversation and its messages.

• conversation: Conversation object (with to_db_dict() method)
• messages: List of Message objects (with to_db_dict() methods)

mgr.save_conversation(conv, conv.messages)

load_conversation(conversation_id: str) -> Conversation | None

Loads a conversation with all messages.

• conversation_id: UUID of the conversation as a string
• Returns: Conversation object or None

conv = mgr.load_conversation("550e8400-e29b-41d4-a716-446655440000")

list_conversations() -> List[Dict[str, Any]]

Lists all conversations (without messages).

• Returns: List of conversation dicts with metadata

all_convs = mgr.list_conversations()
for conv in all_convs:
    print(f"{conv['title']} - {conv['created_at']}")

search_conversations(query: str) -> List[Dict[str, Any]]

Full-text search in titles and message contents.

• query: Search term
• Returns: List of found conversation dicts

results = mgr.search_conversations("Python")

search_by_time(start: str = None, end: str = None) -> List[Dict[str, Any]]

Time-based search for conversations.

• start: Start date (ISO format, e.g. "2025-01-20")
• end: End date (ISO format)
• Returns: List of conversation dicts

# Conversations from today
today = datetime.now().strftime("%Y-%m-%d")
recent = mgr.search_by_time(start=today)

# Conversations from a period
results = mgr.search_by_time(start="2025-01-01", end="2025-01-31")

resume_conversation(conversation_id: str) -> Dict[str, Any] | None

Prepares conversation resumption.

• conversation_id: UUID of the conversation
• Returns: Dict with conversation and last_message

resumed = mgr.resume_conversation(str(conv.id))
last_msg = resumed["last_message"]
print(f"Last message: {last_msg['content']}")

delete_conversation(conversation_id: str)

Deletes a conversation and all associated messages.

• conversation_id: UUID of the conversation

mgr.delete_conversation(str(conv.id))

💡 Advanced Examples

Conversation management with metadata

from datetime import datetime
from qv_session_manager import SessionManager
from qv_ollama_sdk.domain.models import Conversation

mgr = SessionManager()

# Conversation with metadata
conv = Conversation(
    title="JavaScript Tutorial",
    model_name="llama3",
    metadata={
        "topic": "web-development", 
        "difficulty": "beginner",
        "language": "javascript"
    }
)

# Add messages
conv.add_system_message("You are an experienced web developer.")
conv.add_user_message("Explain closures in JavaScript.")

# Save
mgr.save_conversation(conv, conv.messages)

# Search by topic
web_convs = [c for c in mgr.list_conversations() 
             if c.get('metadata', {}).get('topic') == 'web-development']

Batch operations

# Delete all conversations from a specific day
target_date = "2025-01-20"
old_convs = mgr.search_by_time(start=target_date, end=target_date)

for conv in old_convs:
    mgr.delete_conversation(conv['id'])
    print(f"Deleted: {conv['title']}")

Conversation continuation

# Load and extend an existing conversation
conv = mgr.load_conversation("existing-conversation-id")

if conv:
    # Add new messages
    conv.add_user_message("Can you explain that again?")
    conv.add_assistant_message("Sure! Let me rephrase that...")
    
    # Save updated version
    mgr.save_conversation(conv, conv.messages)

🧪 Development & Testing

Run tests

# All tests
pytest

# Specific test
pytest tests/test_session_manager.py

# With output
pytest -v -s

Run demo

python examples/basic_usage.py

Project structure

qv-session-manager/
├── src/qv_session_manager/
│   ├── __init__.py
│   └── session_manager.py
├── tests/
│   └── test_session_manager.py
├── examples/
│   └── basic_usage.py
├── README.md
└── pyproject.toml

🗄️ Database Schema

The SQLite database uses the following schema:

-- Conversations table
CREATE TABLE conversations (
    id TEXT PRIMARY KEY,           -- UUID
    title TEXT,                    -- Conversation title
    created_at TEXT,              -- ISO timestamp
    updated_at TEXT,              -- ISO timestamp  
    metadata TEXT                 -- JSON metadata
);

-- Messages table
CREATE TABLE messages (
    id TEXT PRIMARY KEY,          -- UUID
    conversation_id TEXT,         -- Reference to conversations.id
    role TEXT,                    -- "system", "user", "assistant"
    content TEXT,                 -- Message content
    created_at TEXT,             -- ISO timestamp
    metadata TEXT,               -- JSON metadata
    FOREIGN KEY(conversation_id) REFERENCES conversations(id) ON DELETE CASCADE
);

🤝 Integration with qv-ollama-sdk

from qv_ollama_sdk.client import OllamaClient
from qv_session_manager import SessionManager

# Initialize clients
ollama = OllamaClient()
session_mgr = SessionManager()

# New conversation
conv = ollama.create_conversation(model="gemma3:1b")
conv.title = "Code Review Session"

# Chat with Ollama
response = ollama.chat(conv, "Explain Clean Code principles to me")
print(response.content)

# Persist session
session_mgr.save_conversation(conv, conv.messages)

# Later: load and continue session
loaded_conv = session_mgr.load_conversation(str(conv.id))
next_response = ollama.chat(loaded_conv, "Which tools do you recommend?")

📋 Roadmap

• Advanced search/filter functions (e.g. by metadata)
• Optional encryption of stored data
• Export/import of conversations (JSON, CSV)
• Performance optimizations for large datasets
• Async support for high-performance applications

🐛 Error Handling

try:
    conv = mgr.load_conversation("invalid-id")
    if conv is None:
        print("Conversation not found")
except Exception as e:
    print(f"Error loading: {e}")

📄 License

MIT License - see LICENSE for details.

📞 Support

• Issues: GitHub Issues
• Documentation: This README
• Examples: See the examples/ directory