From c10f1306f511ff80f7d0c21f49439b0b39727a4d Mon Sep 17 00:00:00 2001
From: didi <2020201387@ruc.edu.cn>
Date: Thu, 28 Sep 2023 11:18:22 +0800
Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90=E4=BF=AE=E6=94=B9?=
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完成PR374修改
---
 examples/st_game/memory/retrieve.py                  | 12 ++++++------
 .../{prompts_templates => }/poignancy_chat_v1.txt    |  0
 examples/st_game/prompts/run_gpt_prompts.py          |  8 ++++----
 3 files changed, 10 insertions(+), 10 deletions(-)
 rename examples/st_game/prompts/{prompts_templates => }/poignancy_chat_v1.txt (100%)

diff --git a/examples/st_game/memory/retrieve.py b/examples/st_game/memory/retrieve.py
index 4119524aa..79042df06 100644
--- a/examples/st_game/memory/retrieve.py
+++ b/examples/st_game/memory/retrieve.py
@@ -27,10 +27,10 @@ def agent_retrive(agentmemory:AgentMemory, currtime:datetime, memory_forget:floa
     memories = sorted_memories[:n] if len(sorted_memories) >= n else sorted_memories
 
     Score_list = []
-    Score_list = extract_importance(memories,Score_list)
-    Score_list = extract_recency(currtime,memory_forget,Score_list) 
-    Score_list = extract_relevance(query,Score_list)
-    Score_list = normalize_Socre_floats(Score_list,0,1)
+    Score_list = extract_importance(memories, Score_list)
+    Score_list = extract_recency(currtime, memory_forget, Score_list) 
+    Score_list = extract_relevance(query, Score_list)
+    Score_list = normalize_Socre_floats(Score_list, 0, 1)
 
     total_dict = {} 
     gw = [1,1,1] # 三个因素的权重,重要性，近因性，相关性
@@ -41,7 +41,7 @@ def agent_retrive(agentmemory:AgentMemory, currtime:datetime, memory_forget:floa
         )
         total_dict[Score_list[i]['memory']] = total_score      
     
-    result = top_highest_x_values(total_dict,topk)
+    result = top_highest_x_values(total_dict, topk)
 
     return result
 
@@ -73,7 +73,7 @@ def extract_relevance(query, Score_list):
     query_embedding = embedding_tools(query)
     # 进行
     for i in range(len(Score_list)):
-       result = cos_sim(Score_list[i]["memory"].embedding_key,query_embedding)
+       result = cos_sim(Score_list[i]["memory"].embedding_key, query_embedding)
        Score_list[i]['relevance'] = result
 
     return Score_list
diff --git a/examples/st_game/prompts/prompts_templates/poignancy_chat_v1.txt b/examples/st_game/prompts/poignancy_chat_v1.txt
similarity index 100%
rename from examples/st_game/prompts/prompts_templates/poignancy_chat_v1.txt
rename to examples/st_game/prompts/poignancy_chat_v1.txt
diff --git a/examples/st_game/prompts/run_gpt_prompts.py b/examples/st_game/prompts/run_gpt_prompts.py
index 4c94f3bea..86db1c7c2 100644
--- a/examples/st_game/prompts/run_gpt_prompts.py
+++ b/examples/st_game/prompts/run_gpt_prompts.py
@@ -7,11 +7,11 @@ from memory.scratch import Scratch
 from memory.associative_memory import MemoryBasic
 import json
 
-def run_gpt_prompt_chat_poignancy(scratch:Scratch,content:MemoryBasic.content)->str:
+def get_poignancy_action(scratch:Scratch, content:MemoryBasic.content)->str:
     """
     衡量事件心酸度
     """
-    def create_prompt_input(scratch,content): 
+    def create_prompt_input(scratch, content): 
         prompt_input =  [scratch.name,
                         scratch.iss,
                         scratch.name,
@@ -20,11 +20,11 @@ def run_gpt_prompt_chat_poignancy(scratch:Scratch,content:MemoryBasic.content)->
 
     # 1. Prompt构建
     # 2. Instruction给出
-    prompt_template = "prompt_templates/poignancy_chat_v1.txt" ########
+    prompt_template = "poignancy_chat_v1.txt" ########
     prompt_input = create_prompt_input(scratch, content)  ########
     prompt = prompt_generate(prompt_input, prompt_template)
     special_instruction = "The output should ONLY contain ONE integer value on the scale of 1 to 10."
-    poignancy = special_response_generate(prompt,special_instruction)
+    poignancy = special_response_generate(prompt, special_instruction)
     try:
         poi_dict = json.loads(poignancy)
         return (poi_dict['poignancy'])