修改1.1

metagpt/reflect_and_retrieve/GA_memory_storage.py

@@ -7,7 +7,6 @@
 from run_gpt import run_gpt_prompt_chat_poignancy,run_gpt_random_concept
 from gpt_structure import embedding
 from retrive import agent_retrive
-from reflect import *
 import time
 import json
 
@@ -67,14 +66,14 @@ if __name__ == "__main__":
     John_iss = "John Lin is a pharmacy shopkeeper at the Willow Market and Pharmacy who loves to help people. He is always looking for ways to make the process of getting medication easier for his customers; John Lin is living with his wife, Mei Lin, who is a college professor, and son, Eddy Lin, who is a student studying music theory; John Lin loves his family very much; John Lin has known the old couple next-door, Sam Moore and Jennifer Moore, for a few years; John Lin thinks Sam Moore is a kind and nice man; John Lin knows his neighbor, Yuriko Yamamoto, well; John Lin knows of his neighbors, Tamara Taylor and Carmen Ortiz, but has not met them before; John Lin and Tom Moreno are colleagues at The Willows Market and Pharmacy; John Lin and Tom Moreno are friends and like to discuss local politics together; John Lin knows the Moreno family somewhat well — the husband Tom Moreno and the wife Jane Moreno."
     John = Agent_memeory("John",John_iss,memory_path="agent_memories/John_memory.json")
 
-    # for i in range(3):
-    #     memory = run_gpt_random_concept()
-    #     curr_time = time.time()
-    #     poignancy = run_gpt_prompt_chat_poignancy(John,memory)
-    #     M = Meomry_basic(curr_time,curr_time,memory,poignancy)
-    #     John.memories_list.append(M)
+    for i in range(3):
+        memory = run_gpt_random_concept()
+        curr_time = time.time()
+        poignancy = run_gpt_prompt_chat_poignancy(John,memory)
+        M = Meomry_basic(curr_time,curr_time,memory,poignancy)
+        John.memories_list.append(M)
 
-    # John.memory_save(John.memory_path)
+    John.memory_save(John.memory_path)
 
     for i in range(len(John.memories_list)):
         print(f"John记忆为:{John.memories_list[i].description}")
@@ -85,7 +84,5 @@ if __name__ == "__main__":
     print(f"John的相关信息：{Top_v}")
 
     # John的相关信息：{'Had a friendly chat with Yuriko about her garden.': 2.4992317730827667, 'Helped Mrs. Moore carry groceries into her house.': 1.957656720441911, 'Discussed local politics with Tom Moreno.': 1.9458268038234035}
-    A=generate_focus_point(John.memories_list)
-    B=generate_insights_and_evidence(John,John.memories_list,question=A[0])
         
         

metagpt/reflect_and_retrieve/gpt_structure.py

@@ -5,14 +5,15 @@
 # Date:9.25
 
 import openai
-openai.api_key = "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"
+openai.api_key = "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"
+openai.proxy='http://127.0.0.1:7000'
 # 直接调用Prompt生成
 def response_generate(prompt):
     completion = openai.Completion.create(
         model="gpt-3.5-turbo-instruct",
         prompt= prompt,
         temperature=0,
-        max_tokens = 20,
+        max_tokens = 500,
         top_p = 1,
         stream = False,
         frequency_penalty = 0,

metagpt/reflect_and_retrieve/reflect.py

@@ -1,6 +1,8 @@
 
 import json
 from gpt_structure import final_response
+import run_gpt
+from GA_memory_storage import Agent_memeory
 from retrive import agent_retrive
 '''
 首先
@@ -39,8 +41,28 @@ def generate_insights_and_evidence(agent,memories_list,question, n=5):
     Input:
     {statements}
 
-    What {n} high-level insights can you infer from the above statements? (example format: insight (because of 1, 5, 3))
-    1.'''
+    What {n} high-level insights can you infer from the above statements?
+    You should return a list of list[str,list] . The first element is the insight you have found.The second element is the 
+    '''
     
-    ret = final_response(prompt.format(question=question,statements=statements,n=n), "['insightA',(1,2,3)]")
-    print(ret)
+    ret = final_response(prompt.format(question=question,statements=statements,n=n), "['insightA',[1,2,3]]")
+    try:
+        insight_list = json.loads(ret)
+        return (insight_list)
+    except:
+        return ret
+
+if __name__ == "__main__":
+    # 例子，构建John Agent，实现retrive
+    John_iss = "John Lin is a pharmacy shopkeeper at the Willow Market and Pharmacy who loves to help people. He is always looking for ways to make the process of getting medication easier for his customers; John Lin is living with his wife, Mei Lin, who is a college professor, and son, Eddy Lin, who is a student studying music theory; John Lin loves his family very much; John Lin has known the old couple next-door, Sam Moore and Jennifer Moore, for a few years; John Lin thinks Sam Moore is a kind and nice man; John Lin knows his neighbor, Yuriko Yamamoto, well; John Lin knows of his neighbors, Tamara Taylor and Carmen Ortiz, but has not met them before; John Lin and Tom Moreno are colleagues at The Willows Market and Pharmacy; John Lin and Tom Moreno are friends and like to discuss local politics together; John Lin knows the Moreno family somewhat well — the husband Tom Moreno and the wife Jane Moreno."
+    John = Agent_memeory("John",John_iss,memory_path="agent_memories/John_memory.json")
+
+    # John的相关信息：{'Had a friendly chat with Yuriko about her garden.': 2.4992317730827667, 'Helped Mrs. Moore carry groceries into her house.': 1.957656720441911, 'Discussed local politics with Tom Moreno.': 1.9458268038234035}
+    A=generate_focus_point(John.memories_list)
+    B=generate_insights_and_evidence(John,John.memories_list,question=A[0])
+    print(type(B))
+    print(B)
+    '''
+    这里是输出,list形式，返回给记忆。
+    [['The pharmacy is a friendly and helpful community.', [0, 2, 9, 12]], ['The pharmacy is a place where people come for more than just medication.', [3, 5, 13, 14]], ['The pharmacy is a place where people come for advice and conversation.', [0, 2, 6, 9, 12]], ['The pharmacy is a place where people come for assistance with daily tasks.', [3, 5, 13, 14]], ['The pharmacy is a place where people come for political discussions.', [1]]]
+    '''