wehub-resource-sync
304 lines
8.9 KiB
Markdown
304 lines
8.9 KiB
Markdown
# Advanced SQLite sessions
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`AdvancedSQLiteSession` is an enhanced version of the basic `SQLiteSession` that provides advanced conversation management capabilities including conversation branching, detailed usage analytics, and structured conversation queries.
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## Features
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- **Conversation branching**: Create alternative conversation paths from any user message
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- **Usage tracking**: Detailed token usage analytics per turn with full JSON breakdowns
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- **Structured queries**: Get conversations by turns, tool usage statistics, and more
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- **Branch management**: Independent branch switching and management
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- **Message structure metadata**: Track message types, tool usage, and conversation flow
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## Quick start
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```python
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from agents import Agent, Runner
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from agents.extensions.memory import AdvancedSQLiteSession
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# Create agent
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agent = Agent(
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name="Assistant",
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instructions="Reply very concisely.",
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)
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# Create an advanced session
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session = AdvancedSQLiteSession(
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session_id="conversation_123",
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db_path="conversations.db",
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create_tables=True
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)
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# First conversation turn
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result = await Runner.run(
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agent,
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"What city is the Golden Gate Bridge in?",
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session=session
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)
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print(result.final_output) # "San Francisco"
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# IMPORTANT: Store usage data
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await session.store_run_usage(result)
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# Continue conversation
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result = await Runner.run(
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agent,
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"What state is it in?",
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session=session
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)
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print(result.final_output) # "California"
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await session.store_run_usage(result)
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```
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## Initialization
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```python
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from agents.extensions.memory import AdvancedSQLiteSession
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# Basic initialization
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session = AdvancedSQLiteSession(
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session_id="my_conversation",
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create_tables=True # Auto-create advanced tables
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)
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# With persistent storage
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session = AdvancedSQLiteSession(
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session_id="user_123",
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db_path="path/to/conversations.db",
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create_tables=True
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)
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# With custom logger
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import logging
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logger = logging.getLogger("my_app")
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session = AdvancedSQLiteSession(
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session_id="session_456",
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create_tables=True,
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logger=logger
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)
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```
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### Parameters
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- `session_id` (str): Unique identifier for the conversation session
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- `db_path` (str | Path): Path to SQLite database file. Defaults to `:memory:` for in-memory storage
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- `create_tables` (bool): Whether to automatically create the advanced tables. Defaults to `False`
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- `logger` (logging.Logger | None): Custom logger for the session. Defaults to module logger
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## Usage tracking
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AdvancedSQLiteSession provides detailed usage analytics by storing token usage data per conversation turn. **This is entirely dependent on the `store_run_usage` method being called after each agent run.**
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### Storing usage data
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```python
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# After each agent run, store the usage data
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result = await Runner.run(agent, "Hello", session=session)
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await session.store_run_usage(result)
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# This stores:
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# - Total tokens used
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# - Input/output token breakdown
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# - Request count
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# - Detailed JSON token information (if available)
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```
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### Retrieving usage statistics
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```python
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# Get session-level usage (all branches)
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session_usage = await session.get_session_usage()
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if session_usage:
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print(f"Total requests: {session_usage['requests']}")
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print(f"Total tokens: {session_usage['total_tokens']}")
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print(f"Input tokens: {session_usage['input_tokens']}")
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print(f"Output tokens: {session_usage['output_tokens']}")
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print(f"Total turns: {session_usage['total_turns']}")
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# Get usage for specific branch
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branch_usage = await session.get_session_usage(branch_id="main")
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# Get usage by turn
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turn_usage = await session.get_turn_usage()
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for turn_data in turn_usage:
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print(f"Turn {turn_data['user_turn_number']}: {turn_data['total_tokens']} tokens")
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if turn_data['input_tokens_details']:
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print(f" Input details: {turn_data['input_tokens_details']}")
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if turn_data['output_tokens_details']:
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print(f" Output details: {turn_data['output_tokens_details']}")
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# Get usage for specific turn
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turn_2_usage = await session.get_turn_usage(user_turn_number=2)
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```
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## Conversation branching
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One of the key features of AdvancedSQLiteSession is the ability to create conversation branches from any user message, allowing you to explore alternative conversation paths.
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### Creating branches
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```python
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# Get available turns for branching
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turns = await session.get_conversation_turns()
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for turn in turns:
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print(f"Turn {turn['turn']}: {turn['content']}")
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print(f"Can branch: {turn['can_branch']}")
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# Create a branch from turn 2
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branch_id = await session.create_branch_from_turn(2)
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print(f"Created branch: {branch_id}")
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# Create a branch with custom name
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branch_id = await session.create_branch_from_turn(
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2,
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branch_name="alternative_path"
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)
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# Create branch by searching for content
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branch_id = await session.create_branch_from_content(
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"weather",
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branch_name="weather_focus"
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)
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```
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### Branch management
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```python
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# List all branches
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branches = await session.list_branches()
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for branch in branches:
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current = " (current)" if branch["is_current"] else ""
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print(f"{branch['branch_id']}: {branch['user_turns']} turns, {branch['message_count']} messages{current}")
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# Switch between branches
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await session.switch_to_branch("main")
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await session.switch_to_branch(branch_id)
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# Delete a branch
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await session.delete_branch(branch_id, force=True) # force=True allows deleting current branch
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```
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### Branch workflow example
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```python
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# Original conversation
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result = await Runner.run(agent, "What's the capital of France?", session=session)
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await session.store_run_usage(result)
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result = await Runner.run(agent, "What's the weather like there?", session=session)
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await session.store_run_usage(result)
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# Create branch from turn 2 (weather question)
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branch_id = await session.create_branch_from_turn(2, "weather_focus")
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# Continue in new branch with different question
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result = await Runner.run(
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agent,
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"What are the main tourist attractions in Paris?",
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session=session
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)
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await session.store_run_usage(result)
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# Switch back to main branch
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await session.switch_to_branch("main")
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# Continue original conversation
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result = await Runner.run(
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agent,
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"How expensive is it to visit?",
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session=session
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)
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await session.store_run_usage(result)
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```
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## Structured queries
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AdvancedSQLiteSession provides several methods for analyzing conversation structure and content.
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### Conversation analysis
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```python
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# Get conversation organized by turns
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conversation_by_turns = await session.get_conversation_by_turns()
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for turn_num, items in conversation_by_turns.items():
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print(f"Turn {turn_num}: {len(items)} items")
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for item in items:
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if item["tool_name"]:
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print(f" - {item['type']} (tool: {item['tool_name']})")
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else:
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print(f" - {item['type']}")
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# Get tool usage statistics
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tool_usage = await session.get_tool_usage()
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for tool_name, count, turn in tool_usage:
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print(f"{tool_name}: used {count} times in turn {turn}")
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# Find turns by content
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matching_turns = await session.find_turns_by_content("weather")
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for turn in matching_turns:
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print(f"Turn {turn['turn']}: {turn['content']}")
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```
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### Message structure
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The session automatically tracks message structure including:
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- Message types (user, assistant, tool_call, etc.)
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- Tool names for tool calls
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- Turn numbers and sequence numbers
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- Branch associations
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- Timestamps
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## Database schema
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AdvancedSQLiteSession extends the basic SQLite schema with two additional tables:
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### message_structure table
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```sql
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CREATE TABLE message_structure (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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session_id TEXT NOT NULL,
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message_id INTEGER NOT NULL,
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branch_id TEXT NOT NULL DEFAULT 'main',
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message_type TEXT NOT NULL,
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sequence_number INTEGER NOT NULL,
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user_turn_number INTEGER,
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branch_turn_number INTEGER,
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tool_name TEXT,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (session_id) REFERENCES agent_sessions(session_id) ON DELETE CASCADE,
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FOREIGN KEY (message_id) REFERENCES agent_messages(id) ON DELETE CASCADE
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);
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```
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### turn_usage table
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```sql
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CREATE TABLE turn_usage (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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session_id TEXT NOT NULL,
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branch_id TEXT NOT NULL DEFAULT 'main',
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user_turn_number INTEGER NOT NULL,
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requests INTEGER DEFAULT 0,
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input_tokens INTEGER DEFAULT 0,
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output_tokens INTEGER DEFAULT 0,
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total_tokens INTEGER DEFAULT 0,
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input_tokens_details JSON,
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output_tokens_details JSON,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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FOREIGN KEY (session_id) REFERENCES agent_sessions(session_id) ON DELETE CASCADE,
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UNIQUE(session_id, branch_id, user_turn_number)
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);
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```
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## Complete example
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Check out the [complete example](https://github.com/openai/openai-agents-python/tree/main/examples/memory/advanced_sqlite_session_example.py) for a comprehensive demonstration of all features.
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## API reference
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- [`AdvancedSQLiteSession`][agents.extensions.memory.advanced_sqlite_session.AdvancedSQLiteSession] - Main class
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- [`Session`][agents.memory.session.Session] - Base session protocol
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