Implemented a new multi-scenario dialogue architecture

pilot/conversation.py

@@ -108,8 +108,8 @@ class Conversation:
 conv_default = Conversation(
     system = None,
     roles=("human", "ai"),
-    messages= (),
-    offset=2,
+    messages=[],
+    offset=0,
     sep_style=SeparatorStyle.SINGLE,
     sep="###",
 )

pilot/memory/chat_history/mem_history.py

@@ -0,0 +1,33 @@
+from typing import List
+import json
+import os
+import datetime
+from pilot.memory.chat_history.base import BaseChatHistoryMemory
+from pathlib import Path
+
+from pilot.configs.config import Config
+from pilot.scene.message import (
+    OnceConversation,
+    conversation_from_dict,
+    conversations_to_dict,
+)
+
+
+CFG = Config()
+
+
+class MemHistoryMemory(BaseChatHistoryMemory):
+    histroies_map = {}
+
+    def __init__(self, chat_session_id: str):
+        self.chat_seesion_id = chat_session_id
+        self.histroies_map.update({chat_session_id: []})
+
+    def messages(self) -> List[OnceConversation]:
+        return self.histroies_map.get(self.chat_seesion_id)
+
+    def append(self, once_message: OnceConversation) -> None:
+        self.histroies_map.get(self.chat_seesion_id).append(once_message)
+
+    def clear(self) -> None:
+        self.histroies_map.pop(self.chat_seesion_id)

pilot/scene/base_chat.py

@@ -23,6 +23,7 @@ from pilot.scene.message import OnceConversation
 from pilot.prompts.prompt_new import PromptTemplate
 from pilot.memory.chat_history.base import BaseChatHistoryMemory
 from pilot.memory.chat_history.file_history import FileHistoryMemory
+from pilot.memory.chat_history.mem_history import MemHistoryMemory
 
 from pilot.configs.model_config import LOGDIR, DATASETS_DIR
 from pilot.utils import (
@@ -61,7 +62,10 @@ class BaseChat(ABC):
         self.chat_mode = chat_mode
         self.current_user_input: str = current_user_input
         self.llm_model = CFG.LLM_MODEL
-        ### TODO
+        ### can configurable storage methods
+        # self.memory = MemHistoryMemory(chat_session_id)
+
+        ## TEST
         self.memory = FileHistoryMemory(chat_session_id)
         ### load prompt template
         self.prompt_template: PromptTemplate = CFG.prompt_templates[self.chat_mode.value]

pilot/scene/chat_db/auto_execute/prompt.py

@@ -14,14 +14,12 @@ PROMPT_SCENE_DEFINE = """You are an AI designed to answer human questions, pleas
 _DEFAULT_TEMPLATE = """
 You are a SQL expert. Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
 Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. 
-You can order the results by a relevant column to return the most interesting examples in the database.
-Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
-If the given table is beyond the scope of use, do not use it forcibly.
+Use as few tables as possible when querying.
 Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
 
 """
 
-PROMPT_SUFFIX = """Only use the following tables:
+PROMPT_SUFFIX = """Only use the following tables generate sql:
 {table_info}
 
 Question: {input}