[dev] modify prompt handler

config/test_config.yaml

@@ -0,0 +1,77 @@
+global_config:
+  language: cn
+  max_workers: 5
+  dash_scope_apikey:
+  open_ai_apikey:
+memory_chat:
+  cli_memory_chat:
+    class: chat.cli_memory_chat
+    memory_service: memory_chat_service
+    generation_model: dashscope_generation
+    human_name: human
+    assistant_name: assistant
+memory_service:
+  memory_chat_service:
+    class: memory.service.chat_memory_service
+    history_msg_count: 32
+    contextual_msg_count: 6
+    read_memory_key: read_memory
+    memory_operations:
+      read_message:
+        class: memory.operation.read_memory
+        workflow: dummy_worker
+        description: "read session messages of the user"
+      read_memory:
+        class: memory.operation.read_memory
+        workflow: dummy_worker
+        description: "read related memories of the user"
+      list_memory:
+        class: memory.operation.read_memory
+        workflow: dummy_worker
+        description: "read all memories of the user"
+      write_memory:
+        class: memory.operation.write_memory
+        workflow: dummy_worker
+        description: "write observation memories of the user"
+        interval_time: 60
+      summary_memory:
+        class: memory.operation.summary_memory
+        workflow: dummy_worker
+        description: "summary observation memories of the user"
+        interval_time: 300
+models:
+  dashscope_generation:
+    class: models.llama_index_generation_model
+    module_name: dashscope_generation
+    model_name: qwen-max
+  dashscope_embedding:
+    class: models.llama_index_embedding_model
+    module_name: dashscope_embedding
+    model_name: text-embedding-v2
+  dashscope_rank:
+    class: models.llama_index_rank_model
+    module_name: dashscope_rank
+    model_name: gte-rerank
+vector_store:
+  class: storage.dummy_vector_store
+  embedding_model: dashscope_embedding
+monitor:
+  class: storage.dummy_monitor
+worker:
+  dummy_worker:
+    class: memory.worker.dummy_worker
+    generation_model: dashscope_generation
+    embedding_model: dashscope_embedding
+    rank_model: dashscope_rank
+  retrieve_store_worker:
+    class: memory.worker.read.retrieve_store_worker
+    retrieve_obs_top_k: 100
+    retrieve_ins_pf_top_k: 100
+  fuse_rerank_worker:
+    class: memory.worker.read.fuse_rerank_worker
+    fuse_score_threshold: 0.1
+    fuse_ratio_dict:
+      observation: 1
+    fuse_time_ratio: 2.0
+    fuse_rerank_top_k: 10
+

memory_scope/storage/dummy_vector_store.py

@@ -0,0 +1,20 @@
+from typing import Dict, List
+
+from memory_scope.models.base_model import BaseModel
+from memory_scope.scheme.memory_node import MemoryNode
+from memory_scope.storage.base_vector_store import BaseVectorStore
+
+
+class DummyVectorStore(BaseVectorStore):
+
+    def __init__(self, embedding_model: BaseModel, **kwargs):
+        self.embedding_model: BaseModel = embedding_model
+
+    def retrieve(self, query: str, top_k: int, filter_dict: Dict[str, List[str]]) -> List[MemoryNode]:
+        pass
+
+    async def async_retrieve(self, query: str, top_k: int, filter_dict: Dict[str, List[str]]) -> List[MemoryNode]:
+        pass
+
+    def insert(self, node: MemoryNode):
+        pass

memory_scope/utils/prompt_handler.py

@@ -17,12 +17,13 @@ class PromptHandler(object):
     def add_file_prompts(self, name: str, to_underscore: bool = True):
         if to_underscore:
             name: str = camelcase_to_underscore(name)
+
         class_path = os.path.join(self._default_prompt_dir, name)
         if os.path.exists(f"{class_path}.yaml"):
-            with open(class_path) as f:
+            with open(f"{class_path}.yaml") as f:
                 prompt_language_dict = yaml.load(f, yaml.FullLoader)
         elif os.path.exists(f"{class_path}.json"):
-            with open(class_path) as f:
+            with open(f"{class_path}.json") as f:
                 prompt_language_dict = json.load(f)
         else:
             raise RuntimeError(f"{class_path}.yaml/json is not exists!")
@@ -30,7 +31,7 @@ class PromptHandler(object):
         for key, language_dict in prompt_language_dict.items():
             prompts = language_dict.get(G_CONTEXT.language)
             if not prompts:
-                raise RuntimeError(f"{key}.prompt is empty!")
+                raise RuntimeError(f"{key}.prompt.{G_CONTEXT.language} is empty!")
             self._prompt_dict[key] = prompts
 
     @property

memory_scope/utils/tool_functions.py

@@ -16,7 +16,7 @@ from memory_scope.enumeration.message_role_enum import MessageRoleEnum
 def underscore_to_camelcase(name: str, is_first_title: bool = True):
     name_split = name.split("_")
     if is_first_title:
-        return "".join(x.title() for x in name_split[1:])
+        return "".join(x.title() for x in name_split)
     else:
         return name_split[0] + ''.join(x.title() for x in name_split[1:])