chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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---
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search:
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exclude: true
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---
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# 高级 SQLite 会话
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`AdvancedSQLiteSession` 是基础 `SQLiteSession` 的增强版本，提供高级对话管理能力，包括对话分支、详细的使用情况分析以及结构化对话查询。
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## 功能
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- **对话分支**：从任意用户消息创建替代对话路径
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- **使用情况追踪**：按轮次提供详细的 token 使用情况分析，并包含完整的 JSON 明细
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- **结构化查询**：按轮次获取对话、工具使用统计等
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- **分支管理**：独立的分支切换与管理
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- **消息结构元数据**：跟踪消息类型、工具使用情况和对话流程
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## 快速开始
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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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## 初始化
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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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### 参数
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- `session_id` (str)：对话会话的唯一标识符
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- `db_path` (str | Path)：SQLite 数据库文件路径。默认为 `:memory:`，用于内存存储
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- `create_tables` (bool)：是否自动创建高级表。默认为 `False`
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- `logger` (logging.Logger | None)：用于会话的自定义日志记录器。默认为模块日志记录器
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## 使用情况追踪
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AdvancedSQLiteSession 通过按对话轮次存储 token 使用情况数据，提供详细的使用情况分析。**这完全依赖于在每次智能体运行后调用 `store_run_usage` 方法。**
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### 使用情况数据存储
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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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### 使用情况统计检索
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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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## 对话分支
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AdvancedSQLiteSession 的关键功能之一是能够从任意用户消息创建对话分支，从而让你探索替代的对话路径。
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### 分支创建
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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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### 分支管理
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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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### 分支工作流示例
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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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## 结构化查询
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AdvancedSQLiteSession 提供了多种方法，用于分析对话结构和内容。
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### 对话分析
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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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### 消息结构
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会话会自动跟踪消息结构，包括：
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- 消息类型（用户、assistant、tool_call 等）
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- 工具调用的工具名称
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- 轮次编号和序列号
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- 分支关联
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- 时间戳
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## 数据库架构
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AdvancedSQLiteSession 在基础 SQLite 架构之上扩展了两个额外的表：
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### message_structure 表
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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 表
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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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## 完整示例
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查看[完整示例](https://github.com/openai/openai-agents-python/tree/main/examples/memory/advanced_sqlite_session_example.py)，全面了解所有功能。
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## API 参考
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- [`AdvancedSQLiteSession`][agents.extensions.memory.advanced_sqlite_session.AdvancedSQLiteSession] - 主类
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- [`Session`][agents.memory.session.Session] - 基础会话协议
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@@ -0,0 +1,179 @@
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---
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search:
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exclude: true
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---
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# 加密会话
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`EncryptedSession` 为任何会话实现提供透明加密，通过自动过期旧条目来保护对话数据。
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## 功能
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- **透明加密**：使用 Fernet 加密包装任何会话
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- **每会话密钥**：使用 HKDF 密钥派生，为每个会话生成唯一加密
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- **自动过期**：TTL 过期时会静默跳过旧条目
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- **即插即用替代方案**：适用于任何现有会话实现
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## 安装
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加密会话需要 `encrypt` extra：
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```bash
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pip install openai-agents[encrypt]
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```
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## 快速入门
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```python
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import asyncio
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from agents import Agent, Runner
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from agents.extensions.memory import EncryptedSession, SQLAlchemySession
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async def main():
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agent = Agent("Assistant")
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# Create underlying session
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underlying_session = SQLAlchemySession.from_url(
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"user-123",
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url="sqlite+aiosqlite:///:memory:",
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create_tables=True
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)
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# Wrap with encryption
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session = EncryptedSession(
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session_id="user-123",
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underlying_session=underlying_session,
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encryption_key="your-secret-key-here",
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ttl=600 # 10 minutes
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)
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result = await Runner.run(agent, "Hello", session=session)
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print(result.final_output)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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## 配置
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### 加密密钥
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加密密钥可以是 Fernet 密钥，也可以是任意字符串：
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```python
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from agents.extensions.memory import EncryptedSession
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# Using a Fernet key (base64-encoded)
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session = EncryptedSession(
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session_id="user-123",
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underlying_session=underlying_session,
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encryption_key="your-fernet-key-here",
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ttl=600
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)
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# Using a raw string (will be derived to a key)
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session = EncryptedSession(
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session_id="user-123",
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underlying_session=underlying_session,
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encryption_key="my-secret-password",
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ttl=600
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)
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```
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### TTL（存活时间）
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设置加密条目的有效时长：
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```python
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# Items expire after 1 hour
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session = EncryptedSession(
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session_id="user-123",
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underlying_session=underlying_session,
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encryption_key="secret",
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ttl=3600 # 1 hour in seconds
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)
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# Items expire after 1 day
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session = EncryptedSession(
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session_id="user-123",
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underlying_session=underlying_session,
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encryption_key="secret",
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ttl=86400 # 24 hours in seconds
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)
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```
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## 与不同会话类型的搭配使用
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### 与 SQLite 会话搭配使用
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```python
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from agents import SQLiteSession
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from agents.extensions.memory import EncryptedSession
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# Create encrypted SQLite session
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underlying = SQLiteSession("user-123", "conversations.db")
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session = EncryptedSession(
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session_id="user-123",
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underlying_session=underlying,
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encryption_key="secret-key"
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)
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```
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### 与 SQLAlchemy 会话搭配使用
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```python
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from agents.extensions.memory import EncryptedSession, SQLAlchemySession
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# Create encrypted SQLAlchemy session
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underlying = SQLAlchemySession.from_url(
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"user-123",
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url="postgresql+asyncpg://user:pass@localhost/db",
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create_tables=True
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)
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session = EncryptedSession(
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session_id="user-123",
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underlying_session=underlying,
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encryption_key="secret-key"
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)
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```
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!!! warning "高级会话功能"
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将 `EncryptedSession` 与 `AdvancedSQLiteSession` 等高级会话实现一起使用时，请注意：
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||||
|
||||
- 像 `find_turns_by_content()` 这样的方法无法有效工作，因为消息内容已加密
|
||||
- 基于内容的搜索会在加密数据上运行，因此效果受限
|
||||
|
||||
|
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## 密钥派生
|
||||
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EncryptedSession 使用 HKDF（基于 HMAC 的密钥派生函数）为每个会话派生唯一的加密密钥：
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||||
|
||||
- **主密钥**：你提供的加密密钥
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- **会话盐值**：会话 ID
|
||||
- **信息字符串**：`"agents.session-store.hkdf.v1"`
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- **输出**：32 字节 Fernet 密钥
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|
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这可以确保：
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- 每个会话都有唯一的加密密钥
|
||||
- 没有主密钥就无法派生密钥
|
||||
- 不同会话之间的会话数据无法相互解密
|
||||
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## 自动过期
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||||
|
||||
当条目超过 TTL 时，检索过程中会自动跳过它们：
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||||
|
||||
```python
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||||
# Items older than TTL are silently ignored
|
||||
items = await session.get_items() # Only returns non-expired items
|
||||
|
||||
# Expired items don't affect session behavior
|
||||
result = await Runner.run(agent, "Continue conversation", session=session)
|
||||
```
|
||||
|
||||
## API 参考
|
||||
|
||||
- [`EncryptedSession`][agents.extensions.memory.encrypt_session.EncryptedSession] - 主类
|
||||
- [`Session`][agents.memory.session.Session] - 基础会话协议
|
||||
@@ -0,0 +1,711 @@
|
||||
---
|
||||
search:
|
||||
exclude: true
|
||||
---
|
||||
# 会话
|
||||
|
||||
Agents SDK 提供内置会话记忆，用于在多次智能体运行之间自动维护对话历史记录，从而无需在各轮之间手动处理 `.to_input_list()`。
|
||||
|
||||
会话会存储特定会话的对话历史记录，使智能体能够维护上下文，而无需显式的手动记忆管理。这对于构建聊天应用或多轮对话尤其有用，因为你希望智能体记住之前的交互。
|
||||
|
||||
当你希望 SDK 为你管理客户端侧记忆时，请使用会话。会话不能在同一次运行中与 `conversation_id`、`previous_response_id` 或 `auto_previous_response_id` 结合使用。如果你希望改用由 OpenAI 服务端管理的延续机制，请选择其中一种机制，而不是在其上叠加会话。
|
||||
|
||||
## 快速开始
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner, SQLiteSession
|
||||
|
||||
# Create agent
|
||||
agent = Agent(
|
||||
name="Assistant",
|
||||
instructions="Reply very concisely.",
|
||||
)
|
||||
|
||||
# Create a session instance with a session ID
|
||||
session = SQLiteSession("conversation_123")
|
||||
|
||||
# First turn
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What city is the Golden Gate Bridge in?",
|
||||
session=session
|
||||
)
|
||||
print(result.final_output) # "San Francisco"
|
||||
|
||||
# Second turn - agent automatically remembers previous context
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What state is it in?",
|
||||
session=session
|
||||
)
|
||||
print(result.final_output) # "California"
|
||||
|
||||
# Also works with synchronous runner
|
||||
result = Runner.run_sync(
|
||||
agent,
|
||||
"What's the population?",
|
||||
session=session
|
||||
)
|
||||
print(result.final_output) # "Approximately 39 million"
|
||||
```
|
||||
|
||||
## 使用同一会话恢复中断的运行
|
||||
|
||||
如果某次运行因等待批准而暂停，请使用同一个会话实例（或另一个指向同一后端存储的会话实例）来恢复它，以便恢复后的轮次继续使用同一份已存储的对话历史记录。
|
||||
|
||||
```python
|
||||
result = await Runner.run(agent, "Delete temporary files that are no longer needed.", session=session)
|
||||
|
||||
if result.interruptions:
|
||||
state = result.to_state()
|
||||
for interruption in result.interruptions:
|
||||
state.approve(interruption)
|
||||
result = await Runner.run(agent, state, session=session)
|
||||
```
|
||||
|
||||
## 核心会话行为
|
||||
|
||||
启用会话记忆后：
|
||||
|
||||
1. **每次运行之前**：运行器会自动检索该会话的对话历史记录，并将其前置到输入项中。
|
||||
2. **每次运行之后**：运行期间生成的所有新项（用户输入、助手响应、工具调用等）都会自动存储到会话中。
|
||||
3. **上下文保留**：同一会话的每次后续运行都会包含完整的对话历史记录，使智能体能够维护上下文。
|
||||
|
||||
这消除了手动调用 `.to_input_list()` 并在运行之间管理对话状态的需要。
|
||||
|
||||
## 历史记录与新输入的合并控制
|
||||
|
||||
当你传入会话时，运行器通常会按如下方式准备模型输入：
|
||||
|
||||
1. 会话历史记录（从 `session.get_items(...)` 检索）
|
||||
2. 新轮次输入
|
||||
|
||||
使用 [`RunConfig.session_input_callback`][agents.run.RunConfig.session_input_callback] 在模型调用之前自定义该合并步骤。回调会接收两个列表：
|
||||
|
||||
- `history`：检索到的会话历史记录（已规范化为输入项格式）
|
||||
- `new_input`：当前轮次的新输入项
|
||||
|
||||
返回应发送给模型的最终输入项列表。
|
||||
|
||||
回调接收的是这两个列表的副本，因此你可以安全地修改它们。返回的列表会控制该轮次的模型输入，但 SDK 仍然只会持久化属于新轮次的项。因此，对旧历史记录重新排序或过滤，并不会导致旧会话项被再次作为新输入保存。
|
||||
|
||||
```python
|
||||
from agents import Agent, RunConfig, Runner, SQLiteSession
|
||||
|
||||
|
||||
def keep_recent_history(history, new_input):
|
||||
# Keep only the last 10 history items, then append the new turn.
|
||||
return history[-10:] + new_input
|
||||
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
session = SQLiteSession("conversation_123")
|
||||
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"Continue from the latest updates only.",
|
||||
session=session,
|
||||
run_config=RunConfig(session_input_callback=keep_recent_history),
|
||||
)
|
||||
```
|
||||
|
||||
当你需要自定义裁剪、重新排序或选择性纳入历史记录，同时不改变会话存储项的方式时，请使用此功能。如果你需要在模型调用前立即进行更靠后的最终处理步骤，请使用[运行智能体指南](../running_agents.md)中的 [`call_model_input_filter`][agents.run.RunConfig.call_model_input_filter]。
|
||||
|
||||
## 检索历史记录的限制
|
||||
|
||||
使用 [`SessionSettings`][agents.memory.SessionSettings] 控制每次运行前获取多少历史记录。
|
||||
|
||||
- `SessionSettings(limit=None)`（默认）：检索所有可用的会话项
|
||||
- `SessionSettings(limit=N)`：仅检索最近的 `N` 个项
|
||||
|
||||
你可以通过 [`RunConfig.session_settings`][agents.run.RunConfig.session_settings] 按运行应用此设置：
|
||||
|
||||
```python
|
||||
from agents import Agent, RunConfig, Runner, SessionSettings, SQLiteSession
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
session = SQLiteSession("conversation_123")
|
||||
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"Summarize our recent discussion.",
|
||||
session=session,
|
||||
run_config=RunConfig(session_settings=SessionSettings(limit=50)),
|
||||
)
|
||||
```
|
||||
|
||||
如果你的会话实现公开了默认会话设置，`RunConfig.session_settings` 会为该次运行覆盖任何非 `None` 的值。这对于长对话很有用：你可以在不改变会话默认行为的情况下限制检索大小。
|
||||
|
||||
## 记忆操作
|
||||
|
||||
### 基本操作
|
||||
|
||||
会话支持用于管理对话历史记录的多种操作：
|
||||
|
||||
```python
|
||||
from agents import SQLiteSession
|
||||
|
||||
session = SQLiteSession("user_123", "conversations.db")
|
||||
|
||||
# Get all items in a session
|
||||
items = await session.get_items()
|
||||
|
||||
# Add new items to a session
|
||||
new_items = [
|
||||
{"role": "user", "content": "Hello"},
|
||||
{"role": "assistant", "content": "Hi there!"}
|
||||
]
|
||||
await session.add_items(new_items)
|
||||
|
||||
# Remove and return the most recent item
|
||||
last_item = await session.pop_item()
|
||||
print(last_item) # {"role": "assistant", "content": "Hi there!"}
|
||||
|
||||
# Clear all items from a session
|
||||
await session.clear_session()
|
||||
```
|
||||
|
||||
### 使用 pop_item 进行修正
|
||||
|
||||
当你想撤销或修改对话中的最后一项时，`pop_item` 方法尤其有用：
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner, SQLiteSession
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
session = SQLiteSession("correction_example")
|
||||
|
||||
# Initial conversation
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What's 2 + 2?",
|
||||
session=session
|
||||
)
|
||||
print(f"Agent: {result.final_output}")
|
||||
|
||||
# User wants to correct their question
|
||||
assistant_item = await session.pop_item() # Remove agent's response
|
||||
user_item = await session.pop_item() # Remove user's question
|
||||
|
||||
# Ask a corrected question
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What's 2 + 3?",
|
||||
session=session
|
||||
)
|
||||
print(f"Agent: {result.final_output}")
|
||||
```
|
||||
|
||||
## 内置会话实现
|
||||
|
||||
SDK 为不同用例提供了多个会话实现：
|
||||
|
||||
### 内置会话实现的选择
|
||||
|
||||
在阅读下方详细示例之前，使用此表选择一个起点。
|
||||
|
||||
| 会话类型 | 适用场景 | 备注 |
|
||||
| --- | --- | --- |
|
||||
| `SQLiteSession` | 本地开发和简单应用 | 内置、轻量，可基于文件或内存 |
|
||||
| `AsyncSQLiteSession` | 使用 `aiosqlite` 的异步 SQLite | 支持异步驱动的扩展后端 |
|
||||
| `RedisSession` | 跨多个工作进程/服务的共享记忆 | 适合低延迟分布式部署 |
|
||||
| `SQLAlchemySession` | 使用现有数据库的生产应用 | 适用于 SQLAlchemy 支持的数据库 |
|
||||
| `MongoDBSession` | 已使用 MongoDB 或需要多进程存储的应用 | 异步 pymongo；通过原子序列计数器保证顺序 |
|
||||
| `DaprSession` | 带有 Dapr sidecar 的云原生部署 | 支持多种状态存储，以及 TTL 和一致性控制 |
|
||||
| `OpenAIConversationsSession` | OpenAI 中由服务端管理的存储 | 基于 OpenAI Conversations API 的历史记录 |
|
||||
| `OpenAIResponsesCompactionSession` | 带有自动压缩的长对话 | 另一个会话后端的包装器 |
|
||||
| `AdvancedSQLiteSession` | SQLite 加分支/分析 | 功能集较重；请参阅专门页面 |
|
||||
| `EncryptedSession` | 基于另一个会话的加密 + TTL | 包装器；请先选择底层后端 |
|
||||
|
||||
一些实现有包含更多详细信息的专门页面；这些页面已在其小节中以内联链接形式给出。
|
||||
|
||||
如果你正在为 ChatKit 实现 Python 服务，请使用 `chatkit.store.Store` 实现来持久化 ChatKit 的线程和项。Agents SDK 会话（如 `SQLAlchemySession`）会管理 SDK 侧的对话历史记录，但它们不能直接替代 ChatKit 的 store。请参阅 [`chatkit-python` 关于实现 ChatKit 数据存储的指南](https://github.com/openai/chatkit-python/blob/main/docs/guides/respond-to-user-message.md#implement-your-chatkit-data-store)。
|
||||
|
||||
### OpenAI Conversations API 会话
|
||||
|
||||
通过 `OpenAIConversationsSession` 使用 [OpenAI 的 Conversations API](https://platform.openai.com/docs/api-reference/conversations)。
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner, OpenAIConversationsSession
|
||||
|
||||
# Create agent
|
||||
agent = Agent(
|
||||
name="Assistant",
|
||||
instructions="Reply very concisely.",
|
||||
)
|
||||
|
||||
# Create a new conversation
|
||||
session = OpenAIConversationsSession()
|
||||
|
||||
# Optionally resume a previous conversation by passing a conversation ID
|
||||
# session = OpenAIConversationsSession(conversation_id="conv_123")
|
||||
|
||||
# Start conversation
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What city is the Golden Gate Bridge in?",
|
||||
session=session
|
||||
)
|
||||
print(result.final_output) # "San Francisco"
|
||||
|
||||
# Continue the conversation
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What state is it in?",
|
||||
session=session
|
||||
)
|
||||
print(result.final_output) # "California"
|
||||
```
|
||||
|
||||
### OpenAI Responses 压缩会话
|
||||
|
||||
使用 `OpenAIResponsesCompactionSession` 通过 Responses API（`responses.compact`）压缩已存储的对话历史记录。它会包装一个底层会话，并可根据 `should_trigger_compaction` 在每轮之后自动压缩。不要用它包装 `OpenAIConversationsSession`；这两个功能以不同方式管理历史记录。
|
||||
|
||||
#### 典型用法（自动压缩）
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner, SQLiteSession
|
||||
from agents.memory import OpenAIResponsesCompactionSession
|
||||
|
||||
underlying = SQLiteSession("conversation_123")
|
||||
session = OpenAIResponsesCompactionSession(
|
||||
session_id="conversation_123",
|
||||
underlying_session=underlying,
|
||||
)
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
print(result.final_output)
|
||||
```
|
||||
|
||||
默认情况下，一旦达到候选阈值，压缩会在每轮之后运行。
|
||||
|
||||
当你已经使用 Responses API 响应 ID 串联各轮时，`compaction_mode="previous_response_id"` 效果最好。`compaction_mode="input"` 则会基于当前会话项重建压缩请求，这在响应链不可用，或你希望以会话内容作为事实来源时很有用。默认的 `"auto"` 会选择可用的最安全选项。
|
||||
|
||||
如果你的智能体使用 `ModelSettings(store=False)` 运行，Responses API 不会保留最后一个响应以供之后查找。在这种无状态设置中，默认的 `"auto"` 模式会退回到基于输入的压缩，而不是依赖 `previous_response_id`。有关完整示例，请参阅 [`examples/memory/compaction_session_stateless_example.py`](https://github.com/openai/openai-agents-python/tree/main/examples/memory/compaction_session_stateless_example.py)。
|
||||
|
||||
#### 自动压缩对流式传输的阻塞
|
||||
|
||||
压缩会清空并重写会话历史记录，因此 SDK 会等待压缩完成后才将该运行视为完成。在流式传输模式下，这意味着如果压缩开销较大，在最后一个输出 token 之后，`run.stream_events()` 可能仍会保持打开数秒。
|
||||
|
||||
如果你希望低延迟流式传输或快速轮次切换，请禁用自动压缩，并在轮次之间（或空闲期间）自行调用 `run_compaction()`。你可以根据自己的标准决定何时强制压缩。
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner, SQLiteSession
|
||||
from agents.memory import OpenAIResponsesCompactionSession
|
||||
|
||||
underlying = SQLiteSession("conversation_123")
|
||||
session = OpenAIResponsesCompactionSession(
|
||||
session_id="conversation_123",
|
||||
underlying_session=underlying,
|
||||
# Disable triggering the auto compaction
|
||||
should_trigger_compaction=lambda _: False,
|
||||
)
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
|
||||
# Decide when to compact (e.g., on idle, every N turns, or size thresholds).
|
||||
await session.run_compaction({"force": True})
|
||||
```
|
||||
|
||||
### SQLite 会话
|
||||
|
||||
使用 SQLite 的默认轻量级会话实现：
|
||||
|
||||
```python
|
||||
from agents import SQLiteSession
|
||||
|
||||
# In-memory database (lost when process ends)
|
||||
session = SQLiteSession("user_123")
|
||||
|
||||
# Persistent file-based database
|
||||
session = SQLiteSession("user_123", "conversations.db")
|
||||
|
||||
# Use the session
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"Hello",
|
||||
session=session
|
||||
)
|
||||
```
|
||||
|
||||
### 异步 SQLite 会话
|
||||
|
||||
当你希望使用由 `aiosqlite` 支持的 SQLite 持久化时，请使用 `AsyncSQLiteSession`。
|
||||
|
||||
```bash
|
||||
pip install aiosqlite
|
||||
```
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner
|
||||
from agents.extensions.memory import AsyncSQLiteSession
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
session = AsyncSQLiteSession("user_123", db_path="conversations.db")
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
```
|
||||
|
||||
### Redis 会话
|
||||
|
||||
使用 `RedisSession` 在多个工作进程或服务之间共享会话记忆。
|
||||
|
||||
```bash
|
||||
pip install openai-agents[redis]
|
||||
```
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner
|
||||
from agents.extensions.memory import RedisSession
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
session = RedisSession.from_url(
|
||||
"user_123",
|
||||
url="redis://localhost:6379/0",
|
||||
)
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
```
|
||||
|
||||
### SQLAlchemy 会话
|
||||
|
||||
使用任何 SQLAlchemy 支持的数据库实现的生产就绪型 Agents SDK 会话持久化：
|
||||
|
||||
```python
|
||||
from agents.extensions.memory import SQLAlchemySession
|
||||
|
||||
# Using database URL
|
||||
session = SQLAlchemySession.from_url(
|
||||
"user_123",
|
||||
url="postgresql+asyncpg://user:pass@localhost/db",
|
||||
create_tables=True
|
||||
)
|
||||
|
||||
# Using existing engine
|
||||
from sqlalchemy.ext.asyncio import create_async_engine
|
||||
engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db")
|
||||
session = SQLAlchemySession("user_123", engine=engine, create_tables=True)
|
||||
```
|
||||
|
||||
请参阅 [SQLAlchemy 会话](sqlalchemy_session.md)了解详细文档。
|
||||
|
||||
### Dapr 会话
|
||||
|
||||
当你已经运行 Dapr sidecar，或希望在不更改智能体代码的情况下，让会话存储能够在不同状态存储后端之间迁移时，请使用 `DaprSession`。
|
||||
|
||||
```bash
|
||||
pip install openai-agents[dapr]
|
||||
```
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner
|
||||
from agents.extensions.memory import DaprSession
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
|
||||
async with DaprSession.from_address(
|
||||
"user_123",
|
||||
state_store_name="statestore",
|
||||
dapr_address="localhost:50001",
|
||||
) as session:
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
print(result.final_output)
|
||||
```
|
||||
|
||||
备注：
|
||||
|
||||
- `from_address(...)` 会为你创建并拥有 Dapr 客户端。如果你的应用已经管理了一个客户端，请直接使用 `dapr_client=...` 构造 `DaprSession(...)`。
|
||||
- 当底层状态存储支持 TTL 时，传入 `ttl=...` 可让它自动使旧会话数据过期。
|
||||
- 当你需要更强的写后读保证时，传入 `consistency=DAPR_CONSISTENCY_STRONG`。
|
||||
- Dapr Python SDK 还会检查 HTTP sidecar 端点。在本地开发中，启动 Dapr 时除了 `dapr_address` 中使用的 gRPC 端口外，还应使用 `--dapr-http-port 3500`。
|
||||
- 请参阅 [`examples/memory/dapr_session_example.py`](https://github.com/openai/openai-agents-python/tree/main/examples/memory/dapr_session_example.py)获取完整设置演练，包括本地组件和故障排查。
|
||||
|
||||
|
||||
### MongoDB 会话
|
||||
|
||||
对于已使用 MongoDB 或需要可水平扩展的多进程会话存储的应用，请使用 `MongoDBSession`。
|
||||
|
||||
```bash
|
||||
pip install openai-agents[mongodb]
|
||||
```
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner
|
||||
from agents.extensions.memory import MongoDBSession
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
|
||||
# Create from URI — owns the client and closes it when session.close() is called
|
||||
session = MongoDBSession.from_uri(
|
||||
"user-123",
|
||||
uri="mongodb://localhost:27017",
|
||||
database="agents",
|
||||
)
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
print(result.final_output)
|
||||
await session.close()
|
||||
```
|
||||
|
||||
备注：
|
||||
|
||||
- `from_uri(...)` 会创建并拥有 `AsyncMongoClient`，并在 `session.close()` 时关闭它。如果你的应用已经管理了一个客户端，请直接使用 `client=...` 构造 `MongoDBSession(...)`；在这种情况下，`session.close()` 不执行任何操作，生命周期由调用方管理。
|
||||
- 通过向 `from_uri(...)` 传入 `mongodb+srv://user:password@cluster.example.mongodb.net` URI，即可连接到 [MongoDB Atlas](https://www.mongodb.com/products/platform)，无需其他更改。
|
||||
- 会使用两个集合，且二者名称都可通过 `sessions_collection=`（默认 `agent_sessions`）和 `messages_collection=`（默认 `agent_messages`）配置。首次使用时会自动创建索引。每个消息文档都带有一个单调递增的 `seq` 计数器，可在并发写入者和进程之间保持顺序。
|
||||
- 在首次运行之前，使用 `await session.ping()` 验证连接性。
|
||||
|
||||
### 高级 SQLite 会话
|
||||
|
||||
增强型 SQLite 会话，支持对话分支、用量分析和结构化查询：
|
||||
|
||||
```python
|
||||
from agents.extensions.memory import AdvancedSQLiteSession
|
||||
|
||||
# Create with advanced features
|
||||
session = AdvancedSQLiteSession(
|
||||
session_id="user_123",
|
||||
db_path="conversations.db",
|
||||
create_tables=True
|
||||
)
|
||||
|
||||
# Automatic usage tracking
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
await session.store_run_usage(result) # Track token usage
|
||||
|
||||
# Conversation branching
|
||||
await session.create_branch_from_turn(2) # Branch from turn 2
|
||||
```
|
||||
|
||||
请参阅 [高级 SQLite 会话](advanced_sqlite_session.md)了解详细文档。
|
||||
|
||||
### 加密会话
|
||||
|
||||
用于任何会话实现的透明加密包装器：
|
||||
|
||||
```python
|
||||
from agents.extensions.memory import EncryptedSession, SQLAlchemySession
|
||||
|
||||
# Create underlying session
|
||||
underlying_session = SQLAlchemySession.from_url(
|
||||
"user_123",
|
||||
url="sqlite+aiosqlite:///conversations.db",
|
||||
create_tables=True
|
||||
)
|
||||
|
||||
# Wrap with encryption and TTL
|
||||
session = EncryptedSession(
|
||||
session_id="user_123",
|
||||
underlying_session=underlying_session,
|
||||
encryption_key="your-secret-key",
|
||||
ttl=600 # 10 minutes
|
||||
)
|
||||
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
```
|
||||
|
||||
请参阅 [加密会话](encrypted_session.md)了解详细文档。
|
||||
|
||||
### 其他会话类型
|
||||
|
||||
还有一些其他内置选项。请参阅 `examples/memory/` 以及 `extensions/memory/` 下的源代码。
|
||||
|
||||
## 操作模式
|
||||
|
||||
### 会话 ID 命名
|
||||
|
||||
使用有意义的会话 ID 来帮助你组织对话：
|
||||
|
||||
- 基于用户：`"user_12345"`
|
||||
- 基于线程：`"thread_abc123"`
|
||||
- 基于上下文：`"support_ticket_456"`
|
||||
|
||||
### 记忆持久化
|
||||
|
||||
- 使用内存 SQLite（`SQLiteSession("session_id")`）处理临时对话
|
||||
- 使用基于文件的 SQLite（`SQLiteSession("session_id", "path/to/db.sqlite")`）处理持久对话
|
||||
- 当你需要基于 `aiosqlite` 的实现时，使用异步 SQLite（`AsyncSQLiteSession("session_id", db_path="...")`）
|
||||
- 使用基于 Redis 的会话（`RedisSession.from_url("session_id", url="redis://...")`）实现共享的低延迟会话记忆
|
||||
- 对于使用 SQLAlchemy 支持的现有数据库的生产系统，使用基于 SQLAlchemy 的会话（`SQLAlchemySession("session_id", engine=engine, create_tables=True)`）
|
||||
- 对于已使用 MongoDB 或需要多进程、可水平扩展会话存储的应用，使用 MongoDB 会话（`MongoDBSession.from_uri("session_id", uri="mongodb://localhost:27017")`）
|
||||
- 对于支持 30+ 数据库后端，并内置遥测、追踪和数据隔离的生产级云原生部署，使用 Dapr 状态存储会话（`DaprSession.from_address("session_id", state_store_name="statestore", dapr_address="localhost:50001")`）
|
||||
- 当你希望将历史记录存储在 OpenAI Conversations API 中时，使用 OpenAI 托管存储（`OpenAIConversationsSession()`）
|
||||
- 使用加密会话（`EncryptedSession(session_id, underlying_session, encryption_key)`）为任何会话包装透明加密和基于 TTL 的过期机制
|
||||
- 对于更高级的用例，可以考虑为其他生产系统（例如 Django）实现自定义会话后端
|
||||
|
||||
### 多个会话
|
||||
|
||||
```python
|
||||
from agents import Agent, Runner, SQLiteSession
|
||||
|
||||
agent = Agent(name="Assistant")
|
||||
|
||||
# Different sessions maintain separate conversation histories
|
||||
session_1 = SQLiteSession("user_123", "conversations.db")
|
||||
session_2 = SQLiteSession("user_456", "conversations.db")
|
||||
|
||||
result1 = await Runner.run(
|
||||
agent,
|
||||
"Help me with my account",
|
||||
session=session_1
|
||||
)
|
||||
result2 = await Runner.run(
|
||||
agent,
|
||||
"What are my charges?",
|
||||
session=session_2
|
||||
)
|
||||
```
|
||||
|
||||
### 会话共享
|
||||
|
||||
```python
|
||||
# Different agents can share the same session
|
||||
support_agent = Agent(name="Support")
|
||||
billing_agent = Agent(name="Billing")
|
||||
session = SQLiteSession("user_123")
|
||||
|
||||
# Both agents will see the same conversation history
|
||||
result1 = await Runner.run(
|
||||
support_agent,
|
||||
"Help me with my account",
|
||||
session=session
|
||||
)
|
||||
result2 = await Runner.run(
|
||||
billing_agent,
|
||||
"What are my charges?",
|
||||
session=session
|
||||
)
|
||||
```
|
||||
|
||||
## 完整示例
|
||||
|
||||
下面是展示会话记忆实际效果的完整示例：
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from agents import Agent, Runner, SQLiteSession
|
||||
|
||||
|
||||
async def main():
|
||||
# Create an agent
|
||||
agent = Agent(
|
||||
name="Assistant",
|
||||
instructions="Reply very concisely.",
|
||||
)
|
||||
|
||||
# Create a session instance that will persist across runs
|
||||
session = SQLiteSession("conversation_123", "conversation_history.db")
|
||||
|
||||
print("=== Sessions Example ===")
|
||||
print("The agent will remember previous messages automatically.\n")
|
||||
|
||||
# First turn
|
||||
print("First turn:")
|
||||
print("User: What city is the Golden Gate Bridge in?")
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What city is the Golden Gate Bridge in?",
|
||||
session=session
|
||||
)
|
||||
print(f"Assistant: {result.final_output}")
|
||||
print()
|
||||
|
||||
# Second turn - the agent will remember the previous conversation
|
||||
print("Second turn:")
|
||||
print("User: What state is it in?")
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What state is it in?",
|
||||
session=session
|
||||
)
|
||||
print(f"Assistant: {result.final_output}")
|
||||
print()
|
||||
|
||||
# Third turn - continuing the conversation
|
||||
print("Third turn:")
|
||||
print("User: What's the population of that state?")
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"What's the population of that state?",
|
||||
session=session
|
||||
)
|
||||
print(f"Assistant: {result.final_output}")
|
||||
print()
|
||||
|
||||
print("=== Conversation Complete ===")
|
||||
print("Notice how the agent remembered the context from previous turns!")
|
||||
print("Sessions automatically handles conversation history.")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
## 自定义会话实现
|
||||
|
||||
你可以创建一个遵循 [`Session`][agents.memory.session.Session] 协议的类来实现自己的会话记忆：
|
||||
|
||||
```python
|
||||
from agents.memory.session import SessionABC
|
||||
from agents.items import TResponseInputItem
|
||||
from typing import List
|
||||
|
||||
class MyCustomSession(SessionABC):
|
||||
"""Custom session implementation following the Session protocol."""
|
||||
|
||||
def __init__(self, session_id: str):
|
||||
self.session_id = session_id
|
||||
# Your initialization here
|
||||
|
||||
async def get_items(self, limit: int | None = None) -> List[TResponseInputItem]:
|
||||
"""Retrieve conversation history for this session."""
|
||||
# Your implementation here
|
||||
pass
|
||||
|
||||
async def add_items(self, items: List[TResponseInputItem]) -> None:
|
||||
"""Store new items for this session."""
|
||||
# Your implementation here
|
||||
pass
|
||||
|
||||
async def pop_item(self) -> TResponseInputItem | None:
|
||||
"""Remove and return the most recent item from this session."""
|
||||
# Your implementation here
|
||||
pass
|
||||
|
||||
async def clear_session(self) -> None:
|
||||
"""Clear all items for this session."""
|
||||
# Your implementation here
|
||||
pass
|
||||
|
||||
# Use your custom session
|
||||
agent = Agent(name="Assistant")
|
||||
result = await Runner.run(
|
||||
agent,
|
||||
"Hello",
|
||||
session=MyCustomSession("my_session")
|
||||
)
|
||||
```
|
||||
|
||||
## 社区会话实现
|
||||
|
||||
社区已开发出其他会话实现：
|
||||
|
||||
| 包 | 描述 |
|
||||
|---------|-------------|
|
||||
| [openai-django-sessions](https://pypi.org/project/openai-django-sessions/) | 基于 Django ORM 的会话，适用于任何 Django 支持的数据库（PostgreSQL、MySQL、SQLite 等） |
|
||||
|
||||
如果你构建了一个会话实现，欢迎提交文档 PR，将它添加到这里！
|
||||
|
||||
## API 参考
|
||||
|
||||
有关详细 API 文档，请参阅：
|
||||
|
||||
- [`Session`][agents.memory.session.Session] - 协议接口
|
||||
- [`OpenAIConversationsSession`][agents.memory.OpenAIConversationsSession] - OpenAI Conversations API 实现
|
||||
- [`OpenAIResponsesCompactionSession`][agents.memory.openai_responses_compaction_session.OpenAIResponsesCompactionSession] - Responses API 压缩包装器
|
||||
- [`SQLiteSession`][agents.memory.sqlite_session.SQLiteSession] - 基础 SQLite 实现
|
||||
- [`AsyncSQLiteSession`][agents.extensions.memory.async_sqlite_session.AsyncSQLiteSession] - 基于 `aiosqlite` 的异步 SQLite 实现
|
||||
- [`RedisSession`][agents.extensions.memory.redis_session.RedisSession] - 基于 Redis 的会话实现
|
||||
- [`SQLAlchemySession`][agents.extensions.memory.sqlalchemy_session.SQLAlchemySession] - 基于 SQLAlchemy 的实现
|
||||
- [`MongoDBSession`][agents.extensions.memory.mongodb_session.MongoDBSession] - 基于 MongoDB 的会话实现
|
||||
- [`DaprSession`][agents.extensions.memory.dapr_session.DaprSession] - Dapr 状态存储实现
|
||||
- [`AdvancedSQLiteSession`][agents.extensions.memory.advanced_sqlite_session.AdvancedSQLiteSession] - 支持分支和分析的增强型 SQLite
|
||||
- [`EncryptedSession`][agents.extensions.memory.encrypt_session.EncryptedSession] - 用于任何会话的加密包装器
|
||||
@@ -0,0 +1,80 @@
|
||||
---
|
||||
search:
|
||||
exclude: true
|
||||
---
|
||||
# SQLAlchemy 会话
|
||||
|
||||
`SQLAlchemySession` 使用 SQLAlchemy 提供生产就绪的会话实现，使你可以使用 SQLAlchemy 支持的任何数据库（PostgreSQL、MySQL、SQLite 等）进行会话存储。
|
||||
|
||||
## 安装
|
||||
|
||||
SQLAlchemy 会话需要 `sqlalchemy` 额外依赖：
|
||||
|
||||
```bash
|
||||
pip install openai-agents[sqlalchemy]
|
||||
```
|
||||
|
||||
## 快速开始
|
||||
|
||||
### 使用数据库 URL
|
||||
|
||||
最简单的入门方式：
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from agents import Agent, Runner
|
||||
from agents.extensions.memory import SQLAlchemySession
|
||||
|
||||
async def main():
|
||||
agent = Agent("Assistant")
|
||||
|
||||
# Create session using database URL
|
||||
session = SQLAlchemySession.from_url(
|
||||
"user-123",
|
||||
url="sqlite+aiosqlite:///:memory:",
|
||||
create_tables=True
|
||||
)
|
||||
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
print(result.final_output)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
### 使用现有引擎
|
||||
|
||||
适用于已有 SQLAlchemy 引擎的应用：
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from agents import Agent, Runner
|
||||
from agents.extensions.memory import SQLAlchemySession
|
||||
from sqlalchemy.ext.asyncio import create_async_engine
|
||||
|
||||
async def main():
|
||||
# Create your database engine
|
||||
engine = create_async_engine("postgresql+asyncpg://user:pass@localhost/db")
|
||||
|
||||
agent = Agent("Assistant")
|
||||
session = SQLAlchemySession(
|
||||
"user-456",
|
||||
engine=engine,
|
||||
create_tables=True
|
||||
)
|
||||
|
||||
result = await Runner.run(agent, "Hello", session=session)
|
||||
print(result.final_output)
|
||||
|
||||
# Clean up
|
||||
await engine.dispose()
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
```
|
||||
|
||||
|
||||
## API 参考
|
||||
|
||||
- [`SQLAlchemySession`][agents.extensions.memory.sqlalchemy_session.SQLAlchemySession] - 主类
|
||||
- [`Session`][agents.memory.session.Session] - 基础会话协议
|
||||
Reference in New Issue
Block a user