修改完毕

metagpt/actions/ga_action_base.py

@@ -0,0 +1,84 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# author: didi
+# Date:9.25
+
+import openai
+from metagpt.llm import DEFAULT_LLM
+# 直接调用Prompt生成
+# ga的prompt构建格式和metagpt完全不同。没有办法融合。
+
+
+# 特殊指令加入Prompt生成
+
+
+def final_response(prompt, special_instruction, example_output=None):
+    """
+    通过将特殊指令加入Prompt生成最终的响应。
+
+    参数：
+    - prompt：要生成响应的提示文本。
+    - special_instruction：要加入Prompt的特殊指令。
+    - example_output（可选）：示例输出的JSON字符串。
+
+    返回：
+    生成的最终响应。
+
+    """
+    prompt = '"""\n' + prompt + '\n"""\n'
+    prompt += f"Output the response to the prompt above in json. {special_instruction}\n"
+    if example_output:
+        prompt += "Example output json:\n"
+        prompt += '{"output": "' + str(example_output) + '"}'
+    return DEFAULT_LLM.ask(prompt)
+
+# prompt填充模板
+
+
+def prompt_generate(curr_input, prompt_lib_file):
+    """
+    Takes in the current input (e.g. comment that you want to classifiy) and 
+    the path to a prompt file. The prompt file contains the raw str prompt that
+    will be used, which contains the following substr: !<INPUT>! -- this 
+    function replaces this substr with the actual curr_input to produce the 
+    final promopt that will be sent to the GPT3 server. 
+    ARGS:
+    curr_input: the input we want to feed in (IF THERE ARE MORE THAN ONE
+                INPUT, THIS CAN BE A LIST.)
+    prompt_lib_file: the path to the promopt file. 
+    RETURNS: 
+    a str prompt that will be sent to OpenAI's GPT server.  
+    """
+    if type(curr_input) is type("string"):
+        curr_input = [curr_input]
+        curr_input = [str(i) for i in curr_input]
+
+    f = open(prompt_lib_file, "r")
+    prompt = f.read()
+    f.close()
+    for count, i in enumerate(curr_input):
+        prompt = prompt.replace(f"!<INPUT {count}>!", i)
+    if "<commentblockmarker>###</commentblockmarker>" in prompt:
+        prompt = prompt.split(
+            "<commentblockmarker>###</commentblockmarker>")[1]
+    return prompt.strip()
+
+# 使用OpenAI embedding库进行存储
+
+
+def embedding(query):
+    """
+    Generates an embedding for the given query.
+
+    Args:
+        query (str): The text query to be embedded.
+
+    Returns:
+        str: The embedding key generated for the query.
+    """
+    embedding_result = openai.Embedding.create(
+        model="text-embedding-ada-002",
+        input=query
+    )
+    embedding_key = embedding_result['data'][0]["embedding"]
+    return embedding_key

metagpt/reflect/reflect.py

@@ -3,7 +3,7 @@
 # @Desc   : base class of reflection
 
 import json
-from logging import Logger
+from metagpt.logs import logger
 import time
 from metagpt.actions.ga_action_base import final_response
 '''
@@ -39,7 +39,7 @@ def generate_focus_point(memories_list, n=3):
         return (poi_dict['output'])
     except ValueError:
         print(out)
-        Logger.error('无法返回正常结果')
+        logger.error('无法返回正常结果')
         return out
 
 
@@ -72,7 +72,7 @@ def generate_insights_and_evidence(agent, memories_list, question, n=5):
 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_memory(
+    John = AgentMemory(
         "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}