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https://github.com/TencentCloud/TencentDB-Agent-Memory
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4.2 KiB
4.2 KiB
tencentdb-agent-memory-sdk-python
TencentDB Agent Memory v2 API 的 Python SDK。
提供同步客户端(MemoryClient)和异步客户端(AsyncMemoryClient)。
发布包名:
tencentdb-agent-memory-sdk-python(PyPI /pip install) 导入路径:tencentdb_agent_memory(Python 模块)
安装
# 从 PyPI 安装(发布后)
pip install tencentdb-agent-memory-sdk-python
# 从本地 .whl 安装
pip install ./tencentdb_agent_memory_sdk_python-0.1.0-py3-none-any.whl
快速开始
from tencentdb_agent_memory import MemoryClient
client = MemoryClient(
endpoint="http://127.0.0.1:8420",
api_key="your-api-key",
service_id="your-memory-space-id",
)
# L0: 添加对话
result = client.add_conversation(
session_id="sess-1",
messages=[
{"role": "user", "content": "Hello"},
{"role": "assistant", "content": "Hi!"},
],
)
print(result["accepted_ids"])
# L1: 搜索结构化记忆
hits = client.search_atomic(query="user preferences", limit=5)
print(hits["items"])
# L1: 更新一条记忆
client.update_atomic(id="note-xxx", content="updated content", background="context")
# L2: 列出场景文件
scenarios = client.list_scenarios(path_prefix="")
print(scenarios["entries"])
# L2: 读取场景文件
file = client.read_scenario("工作.md")
print(file["content"])
# L2: 更新场景文件(文件必须已存在)
client.write_scenario("工作.md", "# Updated content", summary="new summary")
# L3: 读取核心记忆(用户画像)
core = client.read_core()
print(core["content"])
# L3: 写入核心记忆
client.write_core("# User Profile\n...")
# Offload v2: 上报工具调用对,触发服务端 L1 异步处理(可 fire-and-forget)
client.offload_ingest(
session_id="agent_sess_123",
tool_pairs=[
{"tool_name": "search", "tool_call_id": "call_1", "params": {"q": "..."}, "result": "...", "timestamp": "..."},
],
)
# Offload v2: 服务端上下文压缩(同步等待结果)
compacted = client.offload_compact(
session_id="agent_sess_123",
messages=[...],
ratio=0.7,
context_window=128000,
)
print(compacted["messages"], compacted["report"])
# 读取记忆 pipeline 产物(如 persona.md、scene_blocks/*.md)
raw = client.read_file("scene_blocks/工作.md")
异步用法
import asyncio
from tencentdb_agent_memory import AsyncMemoryClient
async def main():
async with AsyncMemoryClient(
endpoint="http://127.0.0.1:8420",
api_key="your-api-key",
service_id="your-memory-space-id",
) as client:
result = await client.search_atomic(query="preferences")
print(result["items"])
asyncio.run(main())
API 方法
| 层级 | 方法 | 接口 |
|---|---|---|
| L0 | add_conversation() |
POST /v2/conversation/add |
| L0 | query_conversation() |
POST /v2/conversation/query |
| L0 | search_conversation() |
POST /v2/conversation/search |
| L0 | delete_conversation() |
POST /v2/conversation/delete |
| L1 | update_atomic() |
POST /v2/atomic/update |
| L1 | query_atomic() |
POST /v2/atomic/query |
| L1 | search_atomic() |
POST /v2/atomic/search |
| L1 | delete_atomic() |
POST /v2/atomic/delete |
| L2 | list_scenarios() |
POST /v2/scenario/ls |
| L2 | read_scenario() |
POST /v2/scenario/read |
| L2 | write_scenario() |
POST /v2/scenario/write |
| L2 | rm_scenario() |
POST /v2/scenario/rm |
| L3 | read_core() |
POST /v2/core/read |
| L3 | write_core() |
POST /v2/core/write |
| Offload | offload_ingest() |
POST /v2/offload/ingest |
| Offload | offload_compact() |
POST /v2/offload/compact |
| Offload | offload_query_mmd() |
POST /v2/offload/query-mmd |
错误处理
所有非零 code 的响应会抛出 TDAMError:
from tencentdb_agent_memory import TDAMError
try:
client.read_core()
except TDAMError as e:
print(f"code={e.code} message={e.message} request_id={e.request_id}")
构建与打包
# 构建 wheel
python -m build
# → dist/tencentdb_agent_memory_sdk_python-0.1.0-py3-none-any.whl
# 或仅构建 wheel
pip wheel . --no-deps -w dist/
依赖
httpx>=0.24.0(支持异步的 HTTP 客户端)
许可证
MIT