feat: add ai plugin

Dockerfile

@@ -1,5 +1,5 @@
 # Use the official Python runtime as a parent image
-FROM python:3.12-slim
+FROM python:3.12.3-slim
 
 # Set the working directory to /app
 WORKDIR /app

README.md

@@ -89,12 +89,24 @@ memos:
   token: xxxxxxx
 
 plugins:
-  you_get_plugins:
-    - name: download
-      tag: webhook/download
+  - name: download
+    tag: webhook/download
+    you_get_plugin:
       patterns:
         - https://twitter.com/\w+/status/\d+
         - https://x.com/\w+/status/\d+
+  - name: fix_typos
+    tag: task/fix_typos
+    zhipu_plugin:
+      api_key: xxxxxxx
+      prompt: |
+        You are a fix typos plugin.
+        Please fix the typos in the text.
+
+        The text is:
+        ```
+        {content}
+        ```
 ```
 
 And config definitionn is in [config.py](src/dependencies/config.py)

example/config.yaml

@@ -8,9 +8,35 @@ memos:
   token: xxxxxxx
 
 plugins:
-  you_get_plugins:
-    - name: download
-      tag: webhook/download
+  - name: download
+    tag: webhook/download
+    you_get_plugin:
       patterns:
         - https://twitter.com/\w+/status/\d+
         - https://x.com/\w+/status/\d+
+  - name: fix_typos
+    tag: task/fix_typos
+    zhipu_plugin:
+      api_key: xxxxxxx
+      prompt: |
+        You are a fix typos plugin.
+        Please fix the typos in the text.
+
+        The text is:
+        ```
+        {content}
+        ```
+  - name: translate
+    tag: task/translate
+    zhipu_plugin:
+      api_key: xxxxxxx
+      prompt: |
+        You are a translation plugin.
+        Please translate the text.
+
+        The text is:
+        ```
+        {content}
+        ```
+
+

memos_webhook/app.py

@@ -4,7 +4,8 @@
 import contextlib
 from typing import Annotated, Any
 
-from fastapi import BackgroundTasks, Depends, FastAPI, Request
+from fastapi import BackgroundTasks, Depends, FastAPI, Request, status
+from fastapi.responses import JSONResponse
 
 import memos_webhook.proto_gen.memos.api.v1 as v1
 from memos_webhook.dependencies.config import get_config, new_config
@@ -32,7 +33,7 @@ async def lifespan(app: FastAPI):
         logger.info(f"memos info: {cfg.memos_host}:{cfg.memos_port}")
         logger.info(f"log level: {cfg.log_level}")
         if cfg.plugins:
-            plugin_names = [plugin.name for plugin in cfg.plugins.you_get_plugins]
+            plugin_names = [plugin.name for plugin in cfg.plugins]
             logger.info(f"plugins: {plugin_names}")
         logger.info(f"")
         with new_memos_cli(cfg) as memos_cli:
@@ -77,8 +78,14 @@ async def webhook_handler(
     """The new webhook handler, use protojson."""
     dict_json = await req.json()
     logger.debug(f"webhook handler received request: {dict_json}")
-
-    proto_payload = v1.WebhookRequestPayload().from_dict(dict_json)
+    try:
+        proto_payload = v1.WebhookRequestPayload().from_dict(dict_json)
+    except Exception as e:
+        logger.error(f"parse payload error: {e}")
+        return JSONResponse(
+            content={"code": 1, "message": f"parse payload error: {e}"},
+            status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        )
     background_tasks.add_task(webhook_task, proto_payload, executor)
     return {
         "code": 0,

memos_webhook/dependencies/config.py

@@ -1,3 +1,6 @@
+# mypy: disable-error-code="empty-body"
+from typing import Any
+
 from pydantic import BaseModel
 
 from memos_webhook.utils.config_decorators import (ArgsConfigProvider,
@@ -9,18 +12,39 @@
 
 
 class YouGetPluginConfig(BaseModel):
-    name: str
-    tag: str
     patterns: list[str]
 
 
+class ZhipuPluginConfig(BaseModel):
+    api_key: str
+    prompt: str
+    """Multiple lines for prompt message.
+    Just same format as langchain prompt.
+    Available variables: `{content}`"""
+
+
 class PluginConfig(BaseModel):
-    you_get_plugins: list[YouGetPluginConfig]
+    name: str
+    tag: str
+    you_get_plugin: YouGetPluginConfig | None = None
+    zhipu_plugin: ZhipuPluginConfig | None = None
+
+
+def parse_config_list(raw: Any) -> list[PluginConfig]:
+    if raw is None:
+        return []
+    assert isinstance(raw, list)
+    res: list[PluginConfig] = []
+    for item in raw:
+        res.append(PluginConfig.model_validate(item))
+
+    return res
 
 
 arg_parser = ArgsConfigProvider()
 
 
+# type: ignore
 class Config(BaseUnmarshalConfig, BaseDotenvConfig, BaseArgsConfig):
     @from_env()
     @arg_parser.from_flag(
@@ -81,9 +105,9 @@ def memos_port(self) -> str: ...
     @from_unmarshal("memos", "token")
     def memos_token(self) -> str: ...
 
-    @it_is(PluginConfig)
+    @it_is(list[PluginConfig], transformer=parse_config_list)
     @from_unmarshal()
-    def plugins(self) -> PluginConfig: ...
+    def plugins(self) -> list[PluginConfig]: ...
 
 
 _config: Config

memos_webhook/dependencies/memos_cli.py

@@ -17,7 +17,7 @@ class MemosCli:
     resource_service: v1.ResourceServiceStub
 
 
-_cli: MemosCli = None
+_cli: MemosCli | None = None
 
 
 @contextlib.contextmanager

memos_webhook/dependencies/plugin_manager.py

@@ -1,35 +1,51 @@
-from memos_webhook.plugins.base_plugin import PluginExecutor, PluginProtocol
+from memos_webhook.plugins.base_plugin import IPlugin, PluginExecutor
 from memos_webhook.plugins.you_get_plugin import YouGetPlugin
+from memos_webhook.plugins.zhipu_plugin import ZhipuPlugin
 
 from .config import Config, PluginConfig, YouGetPluginConfig
 from .memos_cli import MemosCli
 
-_plugin_executor: PluginExecutor = None
+_plugin_executor: PluginExecutor | None = None
 
 
-_DEFAULT_PLUGIN_CFG = PluginConfig(
-    you_get_plugins=[
-        YouGetPluginConfig(
-            name="you-get",
-            tag="hook/download",
+_DEFAULT_PLUGIN_CFG = [
+    PluginConfig(
+        name="you-get",
+        tag="hook/download",
+        you_get_plugin=YouGetPluginConfig(
             patterns=[
                 "https://twitter.com/\\w+/status/\\d+",
                 "https://x.com/\\w+/status/\\d+",
             ],
         ),
-    ]
-)
+    )
+]
 
 
 def new_plugin_executor(cfg: Config, memos_cli: MemosCli) -> PluginExecutor:
     global _plugin_executor
-    plugins_cfg = cfg.plugins
-    if plugins_cfg is None:
-        plugins_cfg = _DEFAULT_PLUGIN_CFG  # temp fake cfg
-
-    plugins: list[PluginProtocol] = []
-    for you_get_plugin_cfg in plugins_cfg.you_get_plugins:
-        plugins.append(YouGetPlugin(you_get_plugin_cfg))
+    plugins_cfgs = cfg.plugins
+    if plugins_cfgs is None:
+        plugins_cfgs = _DEFAULT_PLUGIN_CFG  # temp fake cfg
+
+    plugins: list[IPlugin] = []
+    for plugin_cfg in plugins_cfgs:
+        if plugin_cfg.you_get_plugin is not None:
+            plugins.append(
+                YouGetPlugin(
+                    plugin_cfg.name,
+                    plugin_cfg.tag,
+                    plugin_cfg.you_get_plugin,
+                )
+            )
+        if plugin_cfg.zhipu_plugin is not None:
+            plugins.append(
+                ZhipuPlugin(
+                    plugin_cfg.name,
+                    plugin_cfg.tag,
+                    plugin_cfg.zhipu_plugin,
+                )
+            )
 
     _plugin_executor = PluginExecutor(memos_cli, plugins)
     return _plugin_executor

memos_webhook/langchains/zhipu.py

@@ -0,0 +1,92 @@
+from typing import Any, Dict, List, Optional
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.embeddings.embeddings import Embeddings
+from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.language_models.llms import LLM
+from langchain_core.messages import (AIMessage, BaseMessage, HumanMessage,
+                                     SystemMessage)
+from langchain_core.outputs import ChatGeneration, ChatResult
+from pydantic.v1 import root_validator
+from pydantic.v1.main import BaseModel
+from zhipuai import ZhipuAI
+from zhipuai.api_resource.chat.completions import Completions
+
+
+class ZhipuAIChatModel(BaseChatModel):
+    api_key: str = ""
+    temperature: float = 0.9
+    top_p: float = 0.7
+    client: ZhipuAI = None
+    max_tokens: int = 1024
+    model_name: str = "chatglm_turbo"
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        from zhipuai import ZhipuAI
+        from zhipuai.api_resource.chat.completions import Completions
+
+        # if values["client"] is None:
+        values["client"] = ZhipuAI(api_key=values["api_key"])
+        return values
+
+    @property
+    def _llm_type(self) -> str:
+        return "zhipuai"
+
+    def _generate(self,
+                  messages: List[BaseMessage],
+                  stop: List[str] | None = None,
+                  run_manager: CallbackManagerForLLMRun | None = None,
+                  **kwargs: Any) -> ChatResult:
+        completions = Completions(client=self.client)
+        zhipuMessages = [self._langchainMsgToZhipuMsg(m) for m in messages]
+        print(f"{zhipuMessages=}")
+        response = completions.create(
+            model=self.model_name,
+            max_tokens=self.max_tokens,
+            messages=zhipuMessages,
+            temperature=self.temperature,
+            top_p=self.top_p,
+        )
+        print(f"{response=}")
+        generation = ChatGeneration(message=AIMessage(
+            content=response.choices[0].message.content))
+
+        return ChatResult(generations=[generation],
+                          llm_output=response.model_dump())
+
+    def _langchainMsgToZhipuMsg(self, message: BaseMessage) -> Dict:
+        if isinstance(message, AIMessage):
+            return {"role": "assistant", "content": message.content}
+        if isinstance(message, HumanMessage):
+            return {"role": "user", "content": message.content}
+        if isinstance(message, SystemMessage):
+            return {"role": "system", "content": message.content}
+
+        # fallback to human message
+        return {"role": "user", "content": message.content}
+
+
+class ZhipuAIEmbedding(BaseModel, Embeddings):
+    api_key: str = ""
+    client: Any
+    model_name: str = "embedding-2"
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        from zhipuai import ZhipuAI
+        values["client"] = ZhipuAI(api_key=values["api_key"])
+        return values
+
+    def embed_query(self, text: str) -> List[float]:
+        print(f"[embed_query] {text=}")
+        embeddings = self.client.embeddings.create(
+            model=self.model_name,
+            input=text,
+        )
+        return embeddings.data[0].embedding
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        print(f"[embed_documents] {texts=}")
+        return [self.embed_query(text) for text in texts]