feat: merge mgx_ops

examples/exp_pool/init_exp_pool.py

@@ -46,6 +46,7 @@ async def add_exp(req: str, resp: str, tag: str, metric: Metric = None):
         metric=metric or Metric(score=Score(val=10, reason="Manual")),
     )
     exp_manager = get_exp_manager()
+    exp_manager.config.exp_pool.enabled = True
     exp_manager.config.exp_pool.enable_write = True
     exp_manager.create_exp(exp)
     logger.info(f"New experience created for the request `{req[:10]}`.")
@@ -59,8 +60,10 @@ async def add_exps(exps: list, tag: str):
         tag: A tag for categorizing the experiences.
 
     """
-
-    tasks = [add_exp(req=json.dumps(exp["req"]), resp=exp["resp"], tag=tag) for exp in exps]
+    tasks = [
+        add_exp(req=exp["req"] if isinstance(exp["req"], str) else json.dumps(exp["req"]), resp=exp["resp"], tag=tag)
+        for exp in exps
+    ]
     await asyncio.gather(*tasks)
 
 

examples/exp_pool/manager.py

@@ -15,6 +15,7 @@ async def main():
     # Define the simple request and response
     req = "Simple req"
     resp = "Simple resp"
+    exp_manager = get_exp_manager()
 
     # Add the new experience
     exp = Experience(req=req, resp=resp, entry_type=EntryType.MANUAL)

metagpt/exp_pool/decorator.py

@@ -35,8 +35,10 @@ def exp_cache(
     Note:
         1. This can be applied to both synchronous and asynchronous functions.
         2. The function must have a `req` parameter, and it must be provided as a keyword argument.
-        3. If `config.exp_pool.enable_read` is False, the decorator will just directly execute the function.
+        3. If `config.exp_pool.enabled` is False, the decorator will just directly execute the function.
         4. If `config.exp_pool.enable_write` is False, the decorator will skip evaluating and saving the experience.
+        5. If `config.exp_pool.enable_read` is False, the decorator will skip reading from the experience pool.
+
 
     Args:
         _func: Just to make the decorator more flexible, for example, it can be used directly with @exp_cache by default, without the need for @exp_cache().

metagpt/prompts/di/data_analyst.py

@@ -8,7 +8,7 @@ EXTRA_INSTRUCTION = """
     - For information searching requirement, you should use the Browser tool instead of web scraping.
     - When no link is provided, you should use the Browser tool to search for the information.
 7. When you are making plan. It is highly recommend to plan and append all the tasks in first response once time, except for 7.1.
-7.1. When the requirement is given with a file, read the file first through either Editor.read (write code instead for csv or excel) WITHOUT a plan. After reading the file content, use RoleZero.reply_to_human if the requirement can be answered straightaway, otherwise, make a plan if further calculation is needed.
+7.1. When the requirement is inquiring about a pdf, docx, md, or txt document, read the document first through either Editor.read WITHOUT a plan. After reading the document, use RoleZero.reply_to_human if the requirement can be answered straightaway, otherwise, make a plan if further calculation is needed.
 8. Don't finish_current_task multiple times for the same task.
 9. Finish current task timely, such as when the code is written and executed successfully.
 10. When using the command 'end', add the command 'finish_current_task' before it.

metagpt/rag/factories/embedding.py

@@ -29,7 +29,7 @@ class RAGEmbeddingFactory(GenericFactory):
             LLMType.AZURE: self._create_azure,
         }
         super().__init__(creators)
-        self.config = config if self.config else Config.default()
+        self.config = config if config else Config.default()
 
     def get_rag_embedding(self, key: EmbeddingType = None) -> BaseEmbedding:
         """Key is EmbeddingType."""

metagpt/rag/factories/llm.py

@@ -10,7 +10,7 @@ from llama_index.core.llms import (
     LLMMetadata,
 )
 from llama_index.core.llms.callbacks import llm_completion_callback
-from pydantic import Field, model_validator
+from pydantic import Field
 
 from metagpt.config2 import Config
 from metagpt.llm import LLM
@@ -30,19 +30,30 @@ class RAGLLM(CustomLLM):
     num_output: int = -1
     model_name: str = ""
 
-    @model_validator(mode="after")
-    def update_from_config(self):
+    def __init__(
+        self,
+        model_infer: BaseLLM,
+        context_window: int = -1,
+        num_output: int = -1,
+        model_name: str = "",
+        *args,
+        **kwargs
+    ):
+        super().__init__(*args, **kwargs)
         config = Config.default()
-        if self.context_window < 0:
-            self.context_window = TOKEN_MAX.get(config.llm.model, DEFAULT_CONTEXT_WINDOW)
+        if context_window < 0:
+            context_window = TOKEN_MAX.get(config.llm.model, DEFAULT_CONTEXT_WINDOW)
 
-        if self.num_output < 0:
-            self.num_output = config.llm.max_token
+        if num_output < 0:
+            num_output = config.llm.max_token
 
-        if not self.model_name:
-            self.model_name = config.llm.model
+        if not model_name:
+            model_name = config.llm.model
 
-        return self
+        self.model_infer = model_infer
+        self.context_window = context_window
+        self.num_output = num_output
+        self.model_name = model_name
 
     @property
     def metadata(self) -> LLMMetadata:

metagpt/roles/di/role_zero.py

@@ -288,7 +288,9 @@ class RoleZero(Role):
         # routing
         memory = self.get_memories(k=4)  # FIXME: A magic number for two rounds of Q&A
         context = self.llm.format_msg(memory + [UserMessage(content=QUICK_THINK_PROMPT)])
-        intent_result = await self.llm.aask(context, system_msgs=[self.format_quick_system_prompt()])
+        async with ThoughtReporter() as reporter:
+            await reporter.async_report({"type": "classify"})
+            intent_result = await self.llm.aask(context, system_msgs=[self.format_quick_system_prompt()])
 
         if "QUICK" in intent_result or "AMBIGUOUS" in intent_result:  # llm call with the original context
             async with ThoughtReporter(enable_llm_stream=True) as reporter:

tests/metagpt/exp_pool/test_decorator.py

@@ -155,7 +155,10 @@ class TestExpCache:
 
     @pytest.fixture
     def mock_config(self, mocker):
-        return mocker.patch("metagpt.exp_pool.decorator.config")
+        config = Config.default().model_copy(deep=True)
+        default = mocker.patch("metagpt.config2.Config.default")
+        default.return_value = config
+        return config
 
     @pytest.mark.asyncio
     async def test_exp_cache_disabled(self, mock_config, mock_exp_manager):
@@ -171,7 +174,9 @@ class TestExpCache:
 
     @pytest.mark.asyncio
     async def test_exp_cache_enabled_no_perfect_exp(self, mock_config, mock_exp_manager, mock_scorer):
+        mock_config.exp_pool.enabled = True
         mock_config.exp_pool.enable_read = True
+        mock_config.exp_pool.enable_write = True
         mock_exp_manager.query_exps.return_value = []
 
         @exp_cache(manager=mock_exp_manager, scorer=mock_scorer)
@@ -185,6 +190,7 @@ class TestExpCache:
 
     @pytest.mark.asyncio
     async def test_exp_cache_enabled_with_perfect_exp(self, mock_config, mock_exp_manager, mock_perfect_judge):
+        mock_config.exp_pool.enabled = True
         mock_config.exp_pool.enable_read = True
         perfect_exp = Experience(req="test", resp="perfect_result")
         mock_exp_manager.query_exps.return_value = [perfect_exp]