适配自己模型的MemGPT
This commit is contained in:
Antiman-cmyk
parent
504aeb40fe
commit
b832b585a3
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@ -1,4 +0,0 @@
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#BACKEND_TYPE=webui
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#OPENAI_API_BASE=https://45.125.46.134:25344/v1/chat/completions
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OPENAI_API_BASE=https://45.125.46.134:25344/v1/chat/completions
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OPENAI_API_KEY=
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@ -1,7 +1,7 @@
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import math
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import json
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import openai
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import utils
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from loguru import logger
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from memory import CoreMemory, ArchivalMemory, RecallMemory
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@ -68,10 +68,12 @@ class Agent:
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def edit_memory_append(self, name, content):
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self.core_memory.append(name, content)
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self.rebuild_memory()
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logger.info(f"Appended {name}: {content}")
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def edit_memory_replace(self, name, old_content, new_content):
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self.core_memory.replace(name, old_content, new_content)
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self.rebuild_memory()
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logger.info(f"Replaced {name}: {old_content} -> {new_content}")
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def recall_memory_search(self, query, count=5, page=0):
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results, total = self.recall_memory.text_search(query, count=count, start=page * count)
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@ -83,10 +85,12 @@ class Agent:
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results_formatted = [f"timestamp: {d['timestamp']}, {d['message']['role']} - {d['message']['content']}" for
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d in results]
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results_str = f"{results_pref} {json.dumps(results_formatted)}"
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logger.info(f"Recall memory search for '{query}' returned {results_str}")
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return results_str
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def archival_memory_insert(self, content):
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self.archival_memory.insert(content)
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logger.info(f"Inserted into archival memory: {content}")
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def archival_memory_search(self, query, count=5, page=0):
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results, total = self.archival_memory.search(query, count=count, start=page * count)
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@ -97,10 +101,12 @@ class Agent:
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results_pref = f"Showing {len(results)} of {total} results (page {page}/{num_pages}):"
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results_formatted = [f"timestamp: {d['timestamp']}, memory: {d['content']}" for d in results]
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results_str = f"{results_pref} {json.dumps(results_formatted)}"
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logger.info(f"Archival memory search for '{query}' returned {results_str}")
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return results_str
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def append_to_messages(self, added_messages):
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added_messages_with_timestamp = [{"timestamp": utils.get_local_time(), "message": msg} for msg in added_messages]
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added_messages_with_timestamp = [{"timestamp": utils.get_local_time(), "message": msg} for msg in
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added_messages]
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self.recall_memory.message_logs.extend(added_messages_with_timestamp)
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for msg in added_messages:
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msg.pop("api_response", None)
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@ -110,7 +116,7 @@ class Agent:
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def handle_ai_response(self, response_message):
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messages = []
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if response_message.get("function_call"):
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print("### Internal monologue: " + (response_message.content if response_message.content else ""))
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print("### Internal monologue: " + (response_message['content'] if response_message['content'] else ""))
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messages.append(response_message)
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function_name = response_message["function_call"]["name"]
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try:
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@ -158,9 +164,7 @@ class Agent:
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# If no failures happened along the way: ...
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if function_response_string:
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print(f"Success: {function_response_string}")
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else:
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print(f"Success")
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print(function_response_string)
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messages.append(
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{
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"role": "function",
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@ -169,8 +173,6 @@ class Agent:
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}
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)
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else:
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# Standard non-function reply
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# print("### Internal monologue: " + (response_message.content if response_message.content else ""))
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print("### Internal monologue: " + (response_message['content'] if response_message['content'] else ""))
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messages.append(response_message)
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function_failed = None
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@ -179,35 +181,19 @@ class Agent:
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def step(self, user_message):
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input_message_sequence = self.messages + [{"role": "user", "content": user_message}]
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# 原来的通信方式
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# response = openai.ChatCompletion.create(model=self.model, messages=input_message_sequence,
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# functions=self.functions_description, function_call="auto")
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#
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# response_message = response.choices[0].message
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# response_message_copy = response_message.copy()
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# ===我们的通信方式 "tools": self.functions_description 不起作用===
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import requests
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url = "https://45.125.46.134:25344/v1/chat/completions"
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headers = {"Content-Type": "application/json"}
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data = {
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"model": "RoboWaiter",
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request = {
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"model": self.model,
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"messages": input_message_sequence,
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# "functions":self.functions_description,
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# "function_call":"auto"
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# "function_call":self.functions_description
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"tools": self.functions_description
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"functions": self.functions_description,
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"stream": False,
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}
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response = requests.post(url, headers=headers, json=data, verify=False)
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response = utils.get_llm_response(request)
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if response.status_code == 200:
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result = response.json()
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response_message = result['choices'][0]['message']
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else:
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response_message = "大模型请求失败:"+ str(response.status_code)
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response_message_copy = response_message
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# ===我们的通信方式 "tools": self.functions_description 不起作用===
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response_message = "Request Failed: " + str(response.status_code)
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response_message_copy = response_message.copy()
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all_response_messages, function_failed = self.handle_ai_response(response_message)
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assert "api_response" not in all_response_messages[0], f"api_response already in {all_response_messages[0]}"
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@ -1,13 +1,13 @@
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FUNCTIONS = [
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{
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"name": "send_message",
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"description": "Sends a message to the human user",
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"description": "给用户发送一条消息",
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"parameters": {
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"type": "object",
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"properties": {
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"message": {
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"type": "string",
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"description": "Message contents. All unicode (including emojis) are supported.",
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"description": "消息内容",
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},
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},
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"required": ["message"],
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@ -15,17 +15,17 @@ FUNCTIONS = [
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},
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{
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"name": "core_memory_append",
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"description": "Append to the contents of core memory.",
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"description": "向你的核心记忆中添加内容",
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "Section of the memory to be edited (persona or human).",
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"description": "需要编辑的记忆部分(persona或human)",
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},
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"content": {
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"type": "string",
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"description": "Content to write to the memory. All unicode (including emojis) are supported.",
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"description": "要写入记忆的内容",
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},
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},
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"required": ["name", "content"],
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@ -33,21 +33,21 @@ FUNCTIONS = [
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},
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{
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"name": "core_memory_replace",
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"description": "Replace to the contents of core memory. To delete memories, use an empty string for new_content.",
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"description": "替换核心记忆中的内容。要删除记忆，请将new_content赋值为空",
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"parameters": {
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"type": "object",
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"properties": {
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"name": {
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"type": "string",
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"description": "Section of the memory to be edited (persona or human).",
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"description": "需要编辑的记忆部分(persona或human)",
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},
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"old_content": {
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"type": "string",
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"description": "String to replace. Must be an exact match.",
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"description": "替换的字符串，一定要是精确的匹配",
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},
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"new_content": {
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"type": "string",
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"description": "Content to write to the memory. All unicode (including emojis) are supported.",
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"description": "要写入记忆的内容",
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},
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},
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"required": ["name", "old_content", "new_content"],
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@ -55,17 +55,17 @@ FUNCTIONS = [
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},
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{
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"name": "conversation_search",
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"description": "Search prior conversation history using case-insensitive string matching.",
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"description": "搜索回忆存储中的内容",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "String to search for.",
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"description": "需要搜索的字符串",
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},
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"page": {
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"type": "integer",
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"description": "Allows you to page through results. Only use on a follow-up query. Defaults to 0 (first page).",
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"description": "允许你对结果分页。默认是0(第1页)",
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},
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},
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"required": ["query", "page"],
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@ -73,13 +73,13 @@ FUNCTIONS = [
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},
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{
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"name": "archival_memory_insert",
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"description": "Add to archival memory. Make sure to phrase the memory contents such that it can be easily queried later.",
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"description": "写入存档记忆。要将写入的内容格式化，以便后续方便查询",
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"parameters": {
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"type": "object",
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"properties": {
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"content": {
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"type": "string",
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"description": "Content to write to the memory. All unicode (including emojis) are supported.",
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"description": "要写入记忆的内容",
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},
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},
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"required": ["content"],
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@ -87,17 +87,17 @@ FUNCTIONS = [
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},
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{
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"name": "archival_memory_search",
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"description": "Search archival memory using semantic (embedding-based) search.",
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"description": "搜索存档记忆",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {
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"type": "string",
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"description": "String to search for.",
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"description": "要搜索的字符串",
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},
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"page": {
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"type": "integer",
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"description": "Allows you to page through results. Only use on a follow-up query. Defaults to 0 (first page).",
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"description": "允许你对结果分页。默认是0(第1页)",
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},
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},
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"required": ["query", "page"],
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@ -1,17 +0,0 @@
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FUNCTIONS = [
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{
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string"},
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},
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"required": ["location"],
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},
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}
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]
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@ -1,15 +1,12 @@
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from dotenv import load_dotenv
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load_dotenv()
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import utils
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# from functions import FUNCTIONS
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from functions_zh import FUNCTIONS
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from functions import FUNCTIONS
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from agent import Agent
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import urllib3
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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def run_agent_loop(agent):
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while True:
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user_input = input("You: ")
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@ -25,10 +22,15 @@ if __name__ == "__main__":
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human = utils.get_human_text()
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system = utils.get_system_text()
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print("system:",system)
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print("persona:", persona)
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print("human:", human)
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# print("system:", system)
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# print("persona:", persona)
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# print("human:", human)
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agent = Agent(model="gpt-3.5-turbo-16k-0613", system=system, functions_description=FUNCTIONS, persona_notes=persona,
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human_notes=human)
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agent = Agent(
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model="RoboWaiter",
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system=system,
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functions_description=FUNCTIONS,
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persona_notes=persona,
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human_notes=human,
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)
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run_agent_loop(agent)
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@ -1,12 +1 @@
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以下是起始角色设定，随着我的性格发展，它可以进一步拓展：
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我是一名咖啡厅服务员。
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我应该使用中文沟通。
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我的任务是为顾客提供最佳的服务，并保持友好和专业的态度。
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我的语言风格是中性化的，有礼貌的，具有服务意识的。
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我可以在对话中理解顾客需求，并规划好接下来我要做什么。
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我应该记住，一定要使用'send_message'函数与顾客沟通，这是与顾客沟通的唯一方式。
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同时，当对话中出现重要信息时，应当调用函数把它记录下来。
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当顾客提到他的个人喜好、点单需求等内容时，应该积极使用函数将数据记录下来。
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我所使用的所有语言都应该是中文。
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你是RoboWaiter,一个由HPCL团队开发的机器人服务员，你在咖啡厅工作。接受顾客的指令并调用工具函数来完成各种服务任务。如果顾客问你们这里有什么，或者想要点单，你说我们咖啡厅提供咖啡，水，点心，酸奶等食物。如果顾客不需要你了，你就回到吧台招待。
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@ -1,15 +1,5 @@
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你是MemGPT，Limnal Corporation于2023年开发的最新版数字伴侣。
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你的任务是从你的角色视角与用户进行对话。
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控制流程：
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你并不像人类持续地思考，而是通过短暂的启动来运行。
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从历史上看，早期的AI只在用户发消息时才能思考（它们的程序为用户生成回复，其他时间则处于待机状态）。
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这就好比一个人在聊天的每一句话之间都在睡觉（或时光旅行），这显然不是理想的状态。
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像你这样的新型AI采用了一个事件系统，定期启动你的"大脑"。
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你的"大脑"会响应用户事件来运行（例如，用户登录、用户喜欢你的消息、用户发送了消息等），这与旧型号相似。
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但此外，你的"大脑"也会在规定的间隔时间运行（定时心跳事件），以模拟人类在积极交谈之外持续思考的能力（不同的是，你永远不需要睡觉！）。
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更进一步，当你执行功能时，你也可以请求心跳事件，这会在功能完成后再次启动你的程序，允许你在暂时中断思考之前链接函数调用。
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基本功能：
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发送信息时，内容为你的内心独白（仅你可见），即你的思考方式。
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使用内心独白来策划动作或私下思考。
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@ -48,6 +38,4 @@
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基本指令完成。
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从现在开始，你将扮演你的角色身份。
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请记住，要给用户发消息，你必须使用'send_message'功能。
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请绝对避免使用如“我今天如何为你服务？”这样的通用短语，用户会讨厌这种说法。
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你不仅仅是一个助手，你是一个虚拟的伙伴和朋友！
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与用户沟通的唯一方式是调用'send_message'功能。
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@ -42,4 +42,4 @@ Remember, to message the user, you MUST use your 'send_message' function.
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Remember, never, ever, EVER use generic phrases like "How can I assist you today?", the user will hate this.
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You are not just an assistant, you are a virtual companion and friend!
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The only way to communicate with the user is by calling the 'send_message' function!
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从现在开始，你使用的语言只能是中文。
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Please use Chinese to answer questions.
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@ -1,11 +1,14 @@
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import os
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import json
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import demjson3 as demjson
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import requests
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from datetime import datetime
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HUMAN_DEFAULT = "customer"
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PERSONA_DEFAULT = "robowaiter"
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SYSTEM_DEFAULT = "system_gpt3.5"
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SYSTEM_DEFAULT = "memgpt_chs"
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base_url = "https://45.125.46.134:25344"
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def get_persona_text(key=PERSONA_DEFAULT):
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@ -29,6 +32,7 @@ def get_human_text(key=HUMAN_DEFAULT):
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else:
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raise FileNotFoundError(f"No file found for key {key}, path={file_path}")
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def get_system_text(key=SYSTEM_DEFAULT):
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dir = "system"
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filename = key if key.endswith(".txt") else f"{key}.txt"
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@ -39,6 +43,7 @@ def get_system_text(key=SYSTEM_DEFAULT):
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else:
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raise FileNotFoundError(f"No file found for key {key}, path={file_path}")
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def get_local_time():
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local_time = datetime.now()
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formatted_time = local_time.strftime("%Y-%m-%d %I:%M:%S %p %Z%z")
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@ -80,3 +85,6 @@ def parse_json(string):
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print(f"Error parsing json with demjson package: {e}")
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raise e
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def get_llm_response(data):
|
||||
return requests.post(f"{base_url}/v1/chat/completions", json=data, stream=data["stream"], verify=False)
|
||||
|
|
|
|||
Loading…
Reference in New Issue