Merge pull request #33 from codefuse-ai/pr_webui

[feature](webui)<add config_webui for starting app>

.gitignore

@@ -15,3 +15,5 @@ tests
 *egg-info
 build
 dist
+package.sh
+local_config.json

README.md

@@ -123,60 +123,23 @@ cd codefuse-chatbot
 pip install -r requirements.txt
 ```
 
-2、基础配置
+2、启动服务
-
-```bash
-# 修改服务启动的基础配置
-cd configs
-cp model_config.py.example model_config.py
-cp server_config.py.example server_config.py
-
-# model_config#11~12 若需要使用openai接口，openai接口key
-os.environ["OPENAI_API_KEY"] = "sk-xxx"
-# 可自行替换自己需要的api_base_url
-os.environ["API_BASE_URL"] = "https://api.openai.com/v1"
-
-# vi model_config#LLM_MODEL 你需要选择的语言模型
-LLM_MODEL = "gpt-3.5-turbo"
-LLM_MODELs = ["gpt-3.5-turbo"]
-
-# vi model_config#EMBEDDING_MODEL 你需要选择的私有化向量模型
-EMBEDDING_ENGINE = 'model'
-EMBEDDING_MODEL = "text2vec-base"
-
-# vi model_config#embedding_model_dict 修改成你的本地路径，如果能直接连接huggingface则无需修改
-# 若模型地址为：
-model_dir: ~/codefuse-chatbot/embedding_models/shibing624/text2vec-base-chinese
-# 配置如下
-"text2vec-base": "shibing624/text2vec-base-chinese",
-
-# vi server_config#8~14, 推荐采用容器启动服务
-DOCKER_SERVICE = True
-# 是否采用容器沙箱
-SANDBOX_DO_REMOTE = True
-# 是否采用api服务来进行
-NO_REMOTE_API = True
-```
-
-3、启动服务
-
-默认只启动webui相关服务，未启动fastchat（可选）。
-```bash
-# 若需要支撑codellama-34b-int4模型，需要给fastchat打一个补丁
-# cp examples/gptq.py ~/site-packages/fastchat/modules/gptq.py
-# examples/llm_api.py#258 修改为 kwargs={"gptq_wbits": 4},
-
-# start llm-service（可选）
-python examples/llm_api.py
-```
-更多LLM接入方法见[更多细节...](sources/readme_docs/fastchat.md)
-<br>
-
 ```bash
 # 完成server_config.py配置后，可一键启动
 cd examples
-python start.py
+bash start.sh
+# 开始在页面进行配置即可
 ```
+<div align=center>
+  <img src="sources/docs_imgs/webui_config.png" alt="图片">
+</div>
+
+
+或者通过`start.py`进行启动[老版启动方式](sources/readme_docs/start.md)
+更多LLM接入方法见[更多细节...](sources/readme_docs/fastchat.md)
+<br>
+
+
 ## 贡献指南
 非常感谢您对 Codefuse 项目感兴趣，我们非常欢迎您对 Codefuse 项目的各种建议、意见（包括批评）、评论和贡献。
 

README_en.md

@@ -146,57 +146,23 @@ git lfs clone https://huggingface.co/THUDM/chatglm2-6b
 git lfs clone https://huggingface.co/shibing624/text2vec-base-chinese
 ```
 
-4. Basic Configuration
-
-```bash
-# Modify the basic configuration for service startup
-cd configs
-cp model_config.py.example model_config.py
-cp server_config.py.example server_config.py
-
-# model_config#11~12 If you need to use the openai interface, openai interface key
-os.environ["OPENAI_API_KEY"] = "sk-xxx"
-# You can replace the api_base_url yourself
-os.environ["API_BASE_URL"] = "https://api.openai.com/v1"
-
-# vi model_config#105 You need to choose the language model
-LLM_MODEL = "gpt-3.5-turbo"
-
-# vi model_config#43 You need to choose the vector model
-EMBEDDING_MODEL = "text2vec-base"
-
-# vi model_config#25 Modify to your local path, if you can directly connect to huggingface, no modification is needed
-"text2vec-base": "shibing624/text2vec-base-chinese",
-
-# vi server_config#8~14, it is recommended to start the service using containers.
-DOCKER_SERVICE = True
-# Whether to use container sandboxing is up to your specific requirements and preferences
-SANDBOX_DO_REMOTE = True
-# Whether to use api-service to use chatbot
-NO_REMOTE_API = True
-```
-
-5. Start the Service
-
-By default, only webui related services are started, and fastchat is not started (optional).
-```bash
-# if use codellama-34b-int4, you should replace fastchat's gptq.py
-# cp examples/gptq.py ~/site-packages/fastchat/modules/gptq.py
-# examples/llm_api.py#258 => kwargs={"gptq_wbits": 4},
-
-# start llm-service（可选）
-python examples/llm_api.py
-```
-More details about accessing LLM Moldes[More Details...](sources/readme_docs/fastchat.md)
-<br>
 
+4. Start the Service
 ```bash
 # After configuring server_config.py, you can start with just one click.
 cd examples
-bash start_webui.sh
+bash start.sh
+# you can config your llm model and embedding model
 ```
+<div align=center>
+  <img src="sources/docs_imgs/webui_config.png" alt="图片">
+</div>
 
-## 贡献指南
+Or `python start.py` by [old version to start](sources/readme_docs/start-en.md)
+More details about accessing LLM Moldes[More Details...](sources/readme_docs/fastchat.md)
+<br>
+
+## Contribution
 Thank you for your interest in the Codefuse project. We warmly welcome any suggestions, opinions (including criticisms), comments, and contributions to the Codefuse project.
 
 Your suggestions, opinions, and comments on Codefuse can be directly submitted through GitHub Issues.

coagent/base_configs/env_config.py

@@ -1,5 +1,6 @@
 import os
 import platform
+from loguru import logger
 
 system_name = platform.system()
 executable_path = os.getcwd()
@@ -7,8 +8,8 @@ executable_path = os.getcwd()
 # 日志存储路径
 LOG_PATH = os.environ.get("LOG_PATH", None) or os.path.join(executable_path, "logs")
 
-# 知识库默认存储路径
+# # 知识库默认存储路径
-SOURCE_PATH = os.environ.get("SOURCE_PATH", None) or os.path.join(executable_path, "sources")
+# SOURCE_PATH = os.environ.get("SOURCE_PATH", None) or os.path.join(executable_path, "sources")
 
 # 知识库默认存储路径
 KB_ROOT_PATH = os.environ.get("KB_ROOT_PATH", None) or os.path.join(executable_path, "knowledge_base")
@@ -16,8 +17,8 @@ KB_ROOT_PATH = os.environ.get("KB_ROOT_PATH", None) or os.path.join(executable_p
 # 代码库默认存储路径
 CB_ROOT_PATH = os.environ.get("CB_ROOT_PATH", None) or os.path.join(executable_path, "code_base")
 
-# nltk 模型存储路径
+# # nltk 模型存储路径
-NLTK_DATA_PATH = os.environ.get("NLTK_DATA_PATH", None) or os.path.join(executable_path, "nltk_data")
+# NLTK_DATA_PATH = os.environ.get("NLTK_DATA_PATH", None) or os.path.join(executable_path, "nltk_data")
 
 # 代码存储路径
 JUPYTER_WORK_PATH = os.environ.get("JUPYTER_WORK_PATH", None) or os.path.join(executable_path, "jupyter_work")
@@ -31,8 +32,8 @@ NEBULA_PATH = os.environ.get("NEBULA_PATH", None) or os.path.join(executable_pat
 # CHROMA 存储路径
 CHROMA_PERSISTENT_PATH = os.environ.get("CHROMA_PERSISTENT_PATH", None) or os.path.join(executable_path, "data/chroma_data")
 
-for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, CB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NEBULA_PATH, CHROMA_PERSISTENT_PATH]:
+for _path in [LOG_PATH, KB_ROOT_PATH, CB_ROOT_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NEBULA_PATH, CHROMA_PERSISTENT_PATH]:
-    if not os.path.exists(_path):
+    if not os.path.exists(_path) and int(os.environ.get("do_create_dir", True)):
         os.makedirs(_path, exist_ok=True)
 
 # 数据库默认存储路径。

coagent/codechat/codebase_handler/codebase_handler.py

@@ -101,6 +101,7 @@ class CodeBaseHandler:
 
         # get KG info
         if self.nh:
+            time.sleep(10) # aviod nebula staus didn't complete
             stat = self.nh.get_stat()
             vertices_num, edges_num = stat['vertices'], stat['edges']
         else:

coagent/connector/memory_manager.py

@@ -310,7 +310,8 @@ class LocalMemoryManager(BaseMemoryManager):
         # 
         save_to_json_file(memory_messages, file_path)
 
-    def load(self, load_dir: str = "./") -> Memory:
+    def load(self, load_dir: str = None) -> Memory:
+        load_dir = load_dir or self.kb_root_path
         file_path = os.path.join(load_dir, f"{self.user_name}/{self.unique_name}/{self.memory_type}/converation.jsonl")
         uuid_name = "_".join([self.user_name, self.unique_name, self.memory_type])
 
@@ -398,6 +399,7 @@ class LocalMemoryManager(BaseMemoryManager):
     def embedding_retrieval(self, text: str, top_k=1, score_threshold=1.0, user_name: str = "default", **kwargs) -> List[Message]:
         if text is None: return []
         vb_name = f"{user_name}/{self.unique_name}/{self.memory_type}"
+        # logger.debug(f"vb_name={vb_name}")
         vb = KBServiceFactory.get_service(vb_name, "faiss", self.embed_config, self.kb_root_path)
         docs = vb.search_docs(text, top_k=top_k, score_threshold=score_threshold)
         return [Message(**doc.metadata) for doc, score in docs]
@@ -405,11 +407,13 @@ class LocalMemoryManager(BaseMemoryManager):
     def text_retrieval(self, text: str, user_name: str = "default", **kwargs)  -> List[Message]:
         if text is None: return []
         uuid_name = "_".join([user_name, self.unique_name, self.memory_type])
+        # logger.debug(f"uuid_name={uuid_name}")
         return self._text_retrieval_from_cache(self.recall_memory_dict[uuid_name].messages, text, score_threshold=0.3, topK=5, **kwargs)
 
     def datetime_retrieval(self,  datetime: str, text: str = None, n: int = 5, user_name: str = "default", **kwargs) -> List[Message]:
         if datetime is None: return []
         uuid_name = "_".join([user_name, self.unique_name, self.memory_type])
+        # logger.debug(f"uuid_name={uuid_name}")
         return self._datetime_retrieval_from_cache(self.recall_memory_dict[uuid_name].messages, datetime, text, n, **kwargs)
     
     def _text_retrieval_from_cache(self, messages: List[Message], text: str = None, score_threshold=0.3, topK=5, tag_topK=5, **kwargs) -> List[Message]:

coagent/sandbox/pycodebox.py

@@ -7,7 +7,7 @@ from websocket import create_connection
 from websockets.client import WebSocketClientProtocol, ClientConnection
 from websockets.exceptions import ConnectionClosedError
 
-# from configs.model_config import JUPYTER_WORK_PATH
+from coagent.base_configs.env_config import JUPYTER_WORK_PATH
 from .basebox import BaseBox, CodeBoxResponse, CodeBoxStatus
 
 
@@ -21,7 +21,7 @@ class PyCodeBox(BaseBox):
             remote_ip: str = "http://127.0.0.1",
             remote_port: str = "5050",
             token: str = "mytoken",
-            jupyter_work_path: str = "",
+            jupyter_work_path: str = JUPYTER_WORK_PATH,
             do_code_exe: bool = False,
             do_remote: bool = False,
             do_check_net: bool = True,
@@ -30,7 +30,6 @@ class PyCodeBox(BaseBox):
         super().__init__(remote_url, remote_ip, remote_port, token, do_code_exe, do_remote)
         self.enter_status = True
         self.do_check_net = do_check_net
-        self.use_stop = use_stop
         self.jupyter_work_path = jupyter_work_path
         # asyncio.run(self.astart())
         self.start()

coagent/utils/code2doc_util.py

@@ -70,7 +70,8 @@ def encode2md(data, md_format):
     return md_dict
 
 
-method_text_md = '''> {function_name}
+method_text_md = '''
+> {function_name}
 
 | Column Name | Content |
 |-----------------|-----------------|
@@ -79,7 +80,8 @@ method_text_md = '''> {function_name}
 | Return type   | {ReturnType} |
 '''
 
-class_text_md = '''> {code_path}
+class_text_md = '''
+> {code_path}
 
 Bases: {ClassBase}
 

configs/default_config.py

@@ -22,11 +22,14 @@ JUPYTER_WORK_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath
 WEB_CRAWL_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "knowledge_base")
 # NEBULA_DATA存储路径
 NEBULA_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data/nebula_data")
-
+# 语言模型存储路径
+LOCAL_LLM_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "llm_models")
+# 向量模型存储路径
+LOCAL_EM_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "embedding_models")
 # CHROMA 存储路径
 CHROMA_PERSISTENT_PATH = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data/chroma_data")
 
-for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, CB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NEBULA_PATH, CHROMA_PERSISTENT_PATH]:
+for _path in [LOG_PATH, SOURCE_PATH, KB_ROOT_PATH, CB_ROOT_PATH, NLTK_DATA_PATH, JUPYTER_WORK_PATH, WEB_CRAWL_PATH, NEBULA_PATH, CHROMA_PERSISTENT_PATH, LOCAL_LLM_MODEL_DIR, LOCAL_EM_MODEL_DIR]:
     if not os.path.exists(_path):
         os.makedirs(_path, exist_ok=True)
         

configs/model_config.py.example

@@ -4,6 +4,7 @@ import logging
 import torch
 import openai
 import base64
+import json
 from .utils import is_running_in_docker
 from .default_config import *
 # 日志格式
@@ -29,26 +30,35 @@ try:
     client.visit_domain = os.environ.get("visit_domain")
     client.visit_biz = os.environ.get("visit_biz")
     client.visit_biz_line = os.environ.get("visit_biz_line")
-except:
+except Exception as e:
+    OPENAI_API_BASE = "https://api.openai.com/v1"
+    logger.error(e)
     pass
 
+
+try:
+    with open("./local_config.json", "r") as f:
+        update_config = json.load(f)
+except:
+    update_config = {}
+
+
 # add your openai key
-OPENAI_API_BASE = "https://api.openai.com/v1"
+os.environ["API_BASE_URL"] = os.environ.get("API_BASE_URL") or update_config.get("API_BASE_URL") or OPENAI_API_BASE
-os.environ["API_BASE_URL"] = OPENAI_API_BASE
+os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY") or update_config.get("OPENAI_API_KEY") or "sk-xx"
-os.environ["OPENAI_API_KEY"] = "sk-xx"
+openai.api_key = os.environ["OPENAI_API_KEY"]
-openai.api_key = "sk-xx"
 # os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659"
-os.environ["DUCKDUCKGO_PROXY"] = os.environ.get("DUCKDUCKGO_PROXY") or "socks5://127.0.0.1:13659"
+os.environ["DUCKDUCKGO_PROXY"] = os.environ.get("DUCKDUCKGO_PROXY") or update_config.get("DUCKDUCKGO_PROXY") or "socks5h://127.0.0.1:13659"
 # ignore if you dont's use baidu_ocr_api
 os.environ["BAIDU_OCR_API_KEY"] = "xx"
 os.environ["BAIDU_OCR_SECRET_KEY"] = "xx"
 
 os.environ["log_verbose"] = "2"
 # LLM 名称
-EMBEDDING_ENGINE = 'model'  # openai or model
+EMBEDDING_ENGINE = os.environ.get("EMBEDDING_ENGINE") or update_config.get("EMBEDDING_ENGINE") or 'model'  # openai or model
-EMBEDDING_MODEL = "text2vec-base"
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL") or update_config.get("EMBEDDING_MODEL") or "text2vec-base"
-LLM_MODEL = "gpt-3.5-turbo"
+LLM_MODEL = os.environ.get("LLM_MODEL") or "gpt-3.5-turbo"
-LLM_MODELs = ["gpt-3.5-turbo"]
+LLM_MODELs = [LLM_MODEL]
 USE_FASTCHAT = "gpt" not in LLM_MODEL # 判断是否进行fastchat
 
 # LLM 运行设备
@@ -57,10 +67,12 @@ LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mp
 # 在以下字典中修改属性值，以指定本地embedding模型存储位置
 # 如将 "text2vec": "GanymedeNil/text2vec-large-chinese" 修改为 "text2vec": "User/Downloads/text2vec-large-chinese"
 # 此处请写绝对路径
-embedding_model_dict = {
+embedding_model_dict = json.loads(os.environ.get("embedding_model_dict")) if os.environ.get("embedding_model_dict") else {}
+embedding_model_dict = embedding_model_dict or update_config.get("EMBEDDING_MODEL")
+embedding_model_dict = embedding_model_dict or {
     "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
     "ernie-base": "nghuyong/ernie-3.0-base-zh",
-    "text2vec-base": "shibing624/text2vec-base-chinese",
+    "text2vec-base": "text2vec-base-chinese",
     "text2vec": "GanymedeNil/text2vec-large-chinese",
     "text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
     "text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
@@ -74,31 +86,35 @@ embedding_model_dict = {
 }
 
 
-LOCAL_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "embedding_models")
+# LOCAL_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "embedding_models")
-embedding_model_dict = {k: f"/home/user/chatbot/embedding_models/{v}" if is_running_in_docker() else f"{LOCAL_MODEL_DIR}/{v}" for k, v in embedding_model_dict.items()}
+# embedding_model_dict = {k: f"/home/user/chatbot/embedding_models/{v}" if is_running_in_docker() else f"{LOCAL_MODEL_DIR}/{v}" for k, v in embedding_model_dict.items()}
 
 # Embedding 模型运行设备
 EMBEDDING_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
 
-ONLINE_LLM_MODEL = {
+ONLINE_LLM_MODEL = json.loads(os.environ.get("ONLINE_LLM_MODEL")) if os.environ.get("ONLINE_LLM_MODEL") else {}
+ONLINE_LLM_MODEL = ONLINE_LLM_MODEL or update_config.get("ONLINE_LLM_MODEL")
+ONLINE_LLM_MODEL = ONLINE_LLM_MODEL  or {
     # 线上模型。请在server_config中为每个在线API设置不同的端口
 
     "openai-api": {
         "model_name": "gpt-3.5-turbo",
-        "api_base_url": "https://api.openai.com/v1",
+        "api_base_url": OPENAI_API_BASE, # "https://api.openai.com/v1",
         "api_key": "",
         "openai_proxy": "",
     },
     "example": {
-        "version": "gpt-3.5",  # 采用openai接口做示例
+        "version": "gpt-3.5-turbo",  # 采用openai接口做示例
-        "api_base_url": "https://api.openai.com/v1",
+        "api_base_url": OPENAI_API_BASE, # "https://api.openai.com/v1",
         "api_key": "",
         "provider": "ExampleWorker",
     },
 }
 
 # 建议使用chat模型，不要使用base，无法获取正确输出
-llm_model_dict = {
+llm_model_dict = json.loads(os.environ.get("llm_model_dict")) if os.environ.get("llm_model_dict") else {}
+llm_model_dict = llm_model_dict or update_config.get("llm_model_dict")
+llm_model_dict = llm_model_dict or {
     "chatglm-6b": {
         "local_model_path": "THUDM/chatglm-6b",
         "api_base_url": "http://localhost:8888/v1",  # "name"修改为fastchat服务中的"api_base_url"
@@ -147,7 +163,9 @@ llm_model_dict = {
 }
 
 # 建议使用chat模型，不要使用base，无法获取正确输出
-VLLM_MODEL_DICT = {
+VLLM_MODEL_DICT = json.loads(os.environ.get("VLLM_MODEL_DICT")) if os.environ.get("VLLM_MODEL_DICT") else {}
+VLLM_MODEL_DICT = VLLM_MODEL_DICT or update_config.get("VLLM_MODEL_DICT")
+VLLM_MODEL_DICT = VLLM_MODEL_DICT or {
  'chatglm2-6b':  "THUDM/chatglm-6b",
  }
 # 以下模型经过测试可接入，配置仿照上述即可
@@ -157,21 +175,21 @@ VLLM_MODEL_DICT = {
 # 'chatglm3-6b-base', 'Qwen-72B-Chat-Int4'
 
 
-LOCAL_LLM_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "llm_models")
+# LOCAL_LLM_MODEL_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "llm_models")
-# 模型路径重置
+# # 模型路径重置
-llm_model_dict_c = {}
+# llm_model_dict_c = {}
-for k, v in llm_model_dict.items():
+# for k, v in llm_model_dict.items():
-    v_c = {}
+#     v_c = {}
-    for kk, vv in v.items():
+#     for kk, vv in v.items():
-        if k=="local_model_path":
+#         if k=="local_model_path":
-            v_c[kk] = f"/home/user/chatbot/llm_models/{vv}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{vv}" 
+#             v_c[kk] = f"/home/user/chatbot/llm_models/{vv}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{vv}" 
-        else:
+#         else:
-            v_c[kk] = vv
+#             v_c[kk] = vv
-    llm_model_dict_c[k] = v_c
+#     llm_model_dict_c[k] = v_c
 
-llm_model_dict = llm_model_dict_c
+# llm_model_dict = llm_model_dict_c
-# 
+# # 
-VLLM_MODEL_DICT_c = {}
+# VLLM_MODEL_DICT_c = {}
-for k, v in VLLM_MODEL_DICT.items():
+# for k, v in VLLM_MODEL_DICT.items():
-    VLLM_MODEL_DICT_c[k] = f"/home/user/chatbot/llm_models/{v}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{v}" 
+#     VLLM_MODEL_DICT_c[k] = f"/home/user/chatbot/llm_models/{v}" if is_running_in_docker() else f"{LOCAL_LLM_MODEL_DIR}/{v}" 
-VLLM_MODEL_DICT = VLLM_MODEL_DICT_c
+# VLLM_MODEL_DICT = VLLM_MODEL_DICT_c

configs/server_config.py.example

@@ -1,12 +1,24 @@
 from .model_config import LLM_MODEL, LLM_DEVICE
-import os
+import os, json
+
+try:
+    with open("./local_config.json", "r") as f:
+        update_config = json.load(f)
+except:
+    update_config = {}
 
 # API 是否开启跨域，默认为False，如果需要开启，请设置为True
 # is open cross domain
 OPEN_CROSS_DOMAIN = False
 # 是否用容器来启动服务
+try:
+    DOCKER_SERVICE = json.loads(os.environ["DOCKER_SERVICE"]) or update_config.get("DOCKER_SERVICE") or False
+except:
     DOCKER_SERVICE = True
 # 是否采用容器沙箱
+try:
+    SANDBOX_DO_REMOTE = json.loads(os.environ["SANDBOX_DO_REMOTE"]) or update_config.get("SANDBOX_DO_REMOTE") or False
+except:
     SANDBOX_DO_REMOTE = True
 # 是否采用api服务来进行
 NO_REMOTE_API = True
@@ -61,7 +73,7 @@ NEBULA_GRAPH_SERVER = {
 # sandbox api server
 SANDBOX_CONTRAINER_NAME = "devopsgpt_sandbox"
 SANDBOX_IMAGE_NAME = "devopsgpt:py39"
-SANDBOX_HOST = os.environ.get("SANDBOX_HOST") or DEFAULT_BIND_HOST # "172.25.0.3"
+SANDBOX_HOST = os.environ.get("SANDBOX_HOST") or update_config.get("SANDBOX_HOST") or DEFAULT_BIND_HOST # "172.25.0.3"
 SANDBOX_SERVER = {
     "host": f"http://{SANDBOX_HOST}",
     "port": 5050,
@@ -73,7 +85,10 @@ SANDBOX_SERVER = {
 # fastchat model_worker server
 # 这些模型必须是在model_config.llm_model_dict中正确配置的。
 # 在启动startup.py时，可用通过`--model-worker --model-name xxxx`指定模型，不指定则为LLM_MODEL
-FSCHAT_MODEL_WORKERS = {
+# 建议使用chat模型，不要使用base，无法获取正确输出
+FSCHAT_MODEL_WORKERS = json.loads(os.environ.get("FSCHAT_MODEL_WORKERS")) if os.environ.get("FSCHAT_MODEL_WORKERS") else {}
+FSCHAT_MODEL_WORKERS = FSCHAT_MODEL_WORKERS or update_config.get("FSCHAT_MODEL_WORKERS")
+FSCHAT_MODEL_WORKERS = FSCHAT_MODEL_WORKERS or {
     "default": {
         "host": DEFAULT_BIND_HOST,
         "port": 20002,
@@ -117,7 +132,9 @@ FSCHAT_MODEL_WORKERS = {
     'chatglm3-6b-32k': {'host': DEFAULT_BIND_HOST, 'port': 20018},
     'chatglm3-6b-base': {'host': DEFAULT_BIND_HOST, 'port': 20019},
     'Qwen-72B-Chat-Int4': {'host': DEFAULT_BIND_HOST, 'port': 20020},
-    'gpt-3.5-turbo': {'host': DEFAULT_BIND_HOST, 'port': 20021}
+    'gpt-3.5-turbo': {'host': DEFAULT_BIND_HOST, 'port': 20021},
+    'example': {'host': DEFAULT_BIND_HOST, 'port': 20022},
+    'openai-api': {'host': DEFAULT_BIND_HOST, 'port': 20023}
 }
 # fastchat multi model worker server
 FSCHAT_MULTI_MODEL_WORKERS = {

examples/agent_examples/codeChatPhaseLocal_example.py

@@ -41,24 +41,16 @@ embed_config = EmbedConfig(
 
 
 # delete codebase
-codebase_name = 'client_local'
+codebase_name = 'client_nebula'
 code_path = '/Users/bingxu/Desktop/工作/大模型/chatbot/test_code_repo/client'
 code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
 use_nh = True
-# cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
+do_interpret = False
-#                       llm_config=llm_config, embed_config=embed_config)
 cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
                       llm_config=llm_config, embed_config=embed_config)
 cbh.delete_codebase(codebase_name=codebase_name)
 
-
 # initialize codebase
-codebase_name = 'client_local'
-code_path = '/Users/bingxu/Desktop/工作/大模型/chatbot/test_code_repo/client'
-code_path = "D://chromeDownloads/devopschat-bot/client_v2/client"
-code_path = "/home/user/client"
-use_nh = True
-do_interpret = True
 cbh = CodeBaseHandler(codebase_name, code_path, crawl_type='dir', use_nh=use_nh, local_graph_path=CB_ROOT_PATH,
                       llm_config=llm_config, embed_config=embed_config)
 cbh.import_code(do_interpret=do_interpret)
@@ -78,25 +70,25 @@ phase = BasePhase(
 
 ## 需要启动容器中的nebula，采用use_nh=True来构建代码库，是可以通过cypher来查询
 # round-1
-# query_content = "代码一共有多少类"
+query_content = "代码一共有多少类"
-# query = Message(
+query = Message(
-#     role_name="human", role_type="user",
+    role_name="human", role_type="user",
-#     role_content=query_content, input_query=query_content, origin_query=query_content,
+    role_content=query_content, input_query=query_content, origin_query=query_content,
-#     code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
+    code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
-#     )
+    )
-#
+
-# output_message1, _ = phase.step(query)
+output_message1, _ = phase.step(query)
-# print(output_message1)
+print(output_message1)
 
 # round-2
-# query_content = "代码库里有哪些函数，返回5个就行"
+query_content = "代码库里有哪些函数，返回5个就行"
-# query = Message(
+query = Message(
-#     role_name="human", role_type="user",
+    role_name="human", role_type="user",
-#     role_content=query_content, input_query=query_content, origin_query=query_content,
+    role_content=query_content, input_query=query_content, origin_query=query_content,
-#     code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
+    code_engine_name="client_1", score_threshold=1.0, top_k=3, cb_search_type="cypher"
-#     )
+    )
-# output_message2, _ = phase.step(query)
+output_message2, _ = phase.step(query)
-# print(output_message2)
+print(output_message2)
 
 
 # round-3

examples/agent_examples/searchChatPhase_example.py

@@ -7,7 +7,6 @@ sys.path.append(src_dir)
 
 from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH
 from configs.server_config import SANDBOX_SERVER
-from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
 from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
 
 from coagent.connector.phase import BasePhase

examples/model_workers/__init__.py

@@ -16,3 +16,12 @@ from .baichuan import BaiChuanWorker
 from .azure import AzureWorker
 from .tiangong import TianGongWorker
 from .openai import ExampleWorker
+
+
+IMPORT_MODEL_WORKERS = [
+    ChatGLMWorker, MiniMaxWorker, XingHuoWorker, QianFanWorker, FangZhouWorker,
+    QwenWorker, BaiChuanWorker, AzureWorker, TianGongWorker, ExampleWorker
+]
+
+MODEL_WORKER_SETS = [tool.__name__ for tool in IMPORT_MODEL_WORKERS]
+

examples/model_workers/base.py

@@ -1,6 +1,5 @@
 from fastchat.conversation import Conversation
-from configs.model_config import LOG_PATH
+from configs.default_config import LOG_PATH
-# from coagent.base_configs.env_config import LOG_PATH
 import fastchat.constants
 fastchat.constants.LOGDIR = LOG_PATH
 from fastchat.serve.base_model_worker import BaseModelWorker

examples/start.py

@@ -1,4 +1,4 @@
-import docker, sys, os, time, requests, psutil
+import docker, sys, os, time, requests, psutil, json
 import subprocess
 from docker.types import Mount, DeviceRequest
 from loguru import logger
@@ -25,9 +25,6 @@ def check_process(content: str, lang: str = None, do_stop=False):
     '''process-not-exist is true, process-exist is false'''
     for process in psutil.process_iter(["pid", "name", "cmdline"]):
         # check process name contains "jupyter" and port=xx
-
-        # if f"port={SANDBOX_SERVER['port']}" in str(process.info["cmdline"]).lower() and \
-        #     "jupyter" in process.info['name'].lower():
         if content in str(process.info["cmdline"]).lower():
             logger.info(f"content, {process.info}")
             # 关闭进程
@@ -106,7 +103,7 @@ def start_sandbox_service(network_name ='my_network'):
         )
     mounts = [mount]
     # 沙盒的启动与服务的启动是独立的
-    if SANDBOX_SERVER["do_remote"]:
+    if SANDBOX_DO_REMOTE:
         client = docker.from_env()
         networks = client.networks.list()
         if any([network_name==i.attrs["Name"] for i in networks]):
@@ -159,18 +156,6 @@ def start_api_service(sandbox_host=DEFAULT_BIND_HOST):
             target='/home/user/chatbot/',
             read_only=False  # 如果需要只读访问，将此选项设置为True
         )
-        # mount_database = Mount(
-        #     type='bind',
-        #     source=os.path.join(src_dir, "knowledge_base"),
-        #     target='/home/user/knowledge_base/',
-        #     read_only=False  # 如果需要只读访问，将此选项设置为True
-        # )
-        # mount_code_database = Mount(
-        #     type='bind',
-        #     source=os.path.join(src_dir, "code_base"),
-        #     target='/home/user/code_base/',
-        #     read_only=False  # 如果需要只读访问，将此选项设置为True
-        # )
         ports={
                 f"{API_SERVER['docker_port']}/tcp": f"{API_SERVER['port']}/tcp", 
                 f"{WEBUI_SERVER['docker_port']}/tcp": f"{WEBUI_SERVER['port']}/tcp",
@@ -208,6 +193,8 @@ def start_api_service(sandbox_host=DEFAULT_BIND_HOST):
         if check_docker(client, CONTRAINER_NAME, do_stop=True):
             container = start_docker(client, script_shs, ports, IMAGE_NAME, CONTRAINER_NAME, mounts, network=network_name)
 
+        logger.info("You can open http://localhost:8501 to use chatbot!")
+
     else:
         logger.info("start local service")
         # 关闭之前启动的docker 服务
@@ -234,12 +221,17 @@ def start_api_service(sandbox_host=DEFAULT_BIND_HOST):
         
         subprocess.Popen(webui_sh, shell=True)
 
+        logger.info("You can please open http://localhost:8501 to use chatbot!")
 
 
-if __name__ == "__main__":
+def start_main():
+    global SANDBOX_DO_REMOTE, DOCKER_SERVICE
+    SANDBOX_DO_REMOTE = SANDBOX_DO_REMOTE if os.environ.get("SANDBOX_DO_REMOTE") is None else json.loads(os.environ.get("SANDBOX_DO_REMOTE"))
+    DOCKER_SERVICE = DOCKER_SERVICE if os.environ.get("DOCKER_SERVICE") is None else json.loads(os.environ.get("DOCKER_SERVICE"))
+
     start_sandbox_service()
     sandbox_host = DEFAULT_BIND_HOST
-    if SANDBOX_SERVER["do_remote"]:
+    if SANDBOX_DO_REMOTE:
         client = docker.from_env()
         containers = client.containers.list(all=True)
 
@@ -252,3 +244,5 @@ if __name__ == "__main__":
     start_api_service(sandbox_host)
 
 
+if __name__ == "__main__":
+    start_main()

examples/start.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+
+cp ../configs/model_config.py.example ../configs/model_config.py
+cp ../configs/server_config.py.example ../configs/server_config.py
+
+streamlit run webui_config.py --server.port 8510

examples/stop.py

@@ -17,6 +17,8 @@ try:
 except:
     client = None
 
+
+def stop_main():
     # 
     check_docker(client, SANDBOX_CONTRAINER_NAME, do_stop=True, )
     check_process(f"port={SANDBOX_SERVER['port']}", do_stop=True)
@@ -28,3 +30,7 @@ check_process("api.py", do_stop=True)
     check_process("sdfile_api.py", do_stop=True)
     check_process("llm_api.py", do_stop=True)
     check_process("webui.py", do_stop=True)
+
+
+if __name__ == "__main__":
+    stop_main()

examples/webui/document.py

@@ -357,7 +357,7 @@ def knowledge_page(
                 empty.progress(0.0, "")
                 for d in api.recreate_vector_store(
                     kb, vs_type=default_vs_type, embed_model=embedding_model, embedding_device=EMBEDDING_DEVICE,
-                      embed_model_path=embedding_model_dict[EMBEDDING_MODEL], embed_engine=EMBEDDING_ENGINE,
+                      embed_model_path=embedding_model_dict[embedding_model], embed_engine=EMBEDDING_ENGINE,
                       api_key=llm_model_dict[LLM_MODEL]["api_key"],
                       api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                     ):

examples/webui_config.py

@@ -0,0 +1,208 @@
+import streamlit as st
+import docker
+import torch, os, sys, json
+from loguru import logger 
+
+src_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+)
+sys.path.append(src_dir)
+from configs.default_config import *
+
+import platform
+system_name = platform.system()
+
+
+VERSION = "v0.1.0"
+
+MODEL_WORKER_SETS = [
+    "ChatGLMWorker", "MiniMaxWorker", "XingHuoWorker", "QianFanWorker", "FangZhouWorker",
+    "QwenWorker", "BaiChuanWorker", "AzureWorker", "TianGongWorker", "ExampleWorker"
+]
+
+openai_models = ["gpt-3.5-turbo", "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-4"]
+embedding_models = ["openai"]
+
+
+st.write("启动配置页面！")
+
+st.write("如果你要使用语言模型，请将LLM放到 ~/Codefuse-chatbot/llm_models")
+
+st.write("如果你要使用向量模型，请将向量模型放到 ~/Codefuse-chatbot/embedding_models")
+
+with st.container():
+
+    col1, col2 = st.columns(2)
+    with col1.container():
+        llm_model_name = st.selectbox('LLM Model Name', openai_models + [i for i in os.listdir(LOCAL_LLM_MODEL_DIR) if os.path.isdir(os.path.join(LOCAL_LLM_MODEL_DIR, i))])
+
+        llm_apikey = st.text_input('填写 LLM API KEY', 'EMPTY')
+        llm_apiurl = st.text_input('填写 LLM API URL', 'http://localhost:8888/v1')
+
+        llm_engine = st.selectbox('选择哪个llm引擎', ["online", "fastchat", "fastchat-vllm"])
+        llm_model_port = st.text_input('LLM Model Port，非fastchat模式可无视', '20006')
+        llm_provider_option = st.selectbox('选择哪个online模型加载器，非online可无视', ["openai"] + MODEL_WORKER_SETS)
+
+        if llm_engine == "online" and llm_provider_option == "openai":
+            try:
+                from zdatafront import OPENAI_API_BASE
+            except:
+                OPENAI_API_BASE = "https://api.openai.com/v1"
+            llm_apiurl = OPENAI_API_BASE
+
+        device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+        FSCHAT_MODEL_WORKERS = {
+            llm_model_name: {
+                'host': "127.0.0.1", 'port': llm_model_port,
+                "device": device,
+                # todo: 多卡加载需要配置的参数
+                "gpus": None,
+                "numgpus": 1,},
+        }
+
+
+        ONLINE_LLM_MODEL, llm_model_dict, VLLM_MODEL_DICT = {}, {}, {}
+        if llm_engine == "online":
+            ONLINE_LLM_MODEL = {
+                llm_model_name: {
+                    "model_name": llm_model_name,
+                    "version": llm_model_name,
+                    "api_base_url": llm_apiurl, # "https://api.openai.com/v1",
+                    "api_key": llm_apikey,
+                    "openai_proxy": "",
+                    "provider": llm_provider_option
+                },
+            }
+        
+        if llm_engine == "fastchat":
+            llm_model_dict = {
+                llm_model_name: {
+                    "local_model_path": llm_model_name,
+                    "api_base_url": llm_apiurl,  # "name"修改为fastchat服务中的"api_base_url"
+                    "api_key": llm_apikey
+                    }}
+
+
+        if llm_engine == "fastchat-vllm":
+            VLLM_MODEL_DICT = {
+                llm_model_name: {
+                    "local_model_path": llm_model_name,
+                    "api_base_url": llm_apiurl,  # "name"修改为fastchat服务中的"api_base_url"
+                    "api_key": llm_apikey
+                    }
+            }
+            llm_model_dict = {
+                llm_model_name: {
+                    "local_model_path": llm_model_name,
+                    "api_base_url": llm_apiurl,  # "name"修改为fastchat服务中的"api_base_url"
+                    "api_key": llm_apikey
+                    }}
+            
+
+    with col2.container():
+        em_model_name = st.selectbox('Embedding Model Name', [i for i in os.listdir(LOCAL_EM_MODEL_DIR) if os.path.isdir(os.path.join(LOCAL_EM_MODEL_DIR, i))] + embedding_models)
+        em_engine = st.selectbox('选择哪个embedding引擎', ["model", "openai"])
+        device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+        embedding_model_dict = {em_model_name: em_model_name}
+        # em_apikey = st.text_input('Embedding API KEY', '')
+        # em_apiurl = st.text_input('Embedding API URL', '')
+
+# 
+try:
+    client = docker.from_env()
+    has_docker = True
+except:
+    has_docker = False
+
+if has_docker:
+    with st.container():
+        DOCKER_SERVICE = st.toggle('DOCKER_SERVICE', True)
+        SANDBOX_DO_REMOTE = st.toggle('SANDBOX_DO_REMOTE', True)
+else:
+    DOCKER_SERVICE = False
+    SANDBOX_DO_REMOTE = False
+
+
+with st.container():
+    cols = st.columns(3)
+
+    if cols[0].button(
+        "重启服务，按前配置生效",
+        use_container_width=True,
+    ):
+        from start import start_main
+        from stop import stop_main
+        stop_main()
+        start_main()
+    if cols[1].button(
+        "停止服务",
+        use_container_width=True,
+    ):
+        from stop import stop_main
+        stop_main()
+
+    if cols[2].button(
+        "启动对话服务",
+        use_container_width=True
+    ):
+        
+        os.environ["API_BASE_URL"] = llm_apiurl
+        os.environ["OPENAI_API_KEY"] = llm_apikey
+
+        os.environ["EMBEDDING_ENGINE"] = em_engine
+        os.environ["EMBEDDING_MODEL"] = em_model_name
+        os.environ["LLM_MODEL"] = llm_model_name
+
+        embedding_model_dict = {k: f"/home/user/chatbot/embedding_models/{v}" if DOCKER_SERVICE else f"{LOCAL_EM_MODEL_DIR}/{v}" for k, v in embedding_model_dict.items()}
+        os.environ["embedding_model_dict"] = json.dumps(embedding_model_dict)
+
+        os.environ["ONLINE_LLM_MODEL"] = json.dumps(ONLINE_LLM_MODEL)
+
+        # 模型路径重置
+        llm_model_dict_c = {}
+        for k, v in llm_model_dict.items():
+            v_c = {}
+            for kk, vv in v.items():
+                if k=="local_model_path":
+                    v_c[kk] = f"/home/user/chatbot/llm_models/{vv}" if DOCKER_SERVICE else f"{LOCAL_LLM_MODEL_DIR}/{vv}" 
+                else:
+                    v_c[kk] = vv
+            llm_model_dict_c[k] = v_c
+
+        llm_model_dict = llm_model_dict_c
+        os.environ["llm_model_dict"] = json.dumps(llm_model_dict)
+        # 
+        VLLM_MODEL_DICT_c = {}
+        for k, v in VLLM_MODEL_DICT.items():
+            VLLM_MODEL_DICT_c[k] = f"/home/user/chatbot/llm_models/{v}" if DOCKER_SERVICE else f"{LOCAL_LLM_MODEL_DIR}/{v}" 
+        VLLM_MODEL_DICT = VLLM_MODEL_DICT_c
+        os.environ["VLLM_MODEL_DICT"] = json.dumps(VLLM_MODEL_DICT)
+
+        # server config
+        os.environ["DOCKER_SERVICE"] = json.dumps(DOCKER_SERVICE)
+        os.environ["SANDBOX_DO_REMOTE"] = json.dumps(SANDBOX_DO_REMOTE)
+        os.environ["FSCHAT_MODEL_WORKERS"] = json.dumps(FSCHAT_MODEL_WORKERS)
+
+        update_json = {
+            "API_BASE_URL": llm_apiurl,
+            "OPENAI_API_KEY": llm_apikey,
+            "EMBEDDING_ENGINE": em_engine,
+            "EMBEDDING_MODEL": em_model_name,
+            "LLM_MODEL": llm_model_name,
+            "embedding_model_dict": json.dumps(embedding_model_dict),
+            "llm_model_dict": json.dumps(llm_model_dict),
+            "ONLINE_LLM_MODEL": json.dumps(ONLINE_LLM_MODEL),
+            "VLLM_MODEL_DICT": json.dumps(VLLM_MODEL_DICT),
+            "DOCKER_SERVICE": json.dumps(DOCKER_SERVICE),
+            "SANDBOX_DO_REMOTE": json.dumps(SANDBOX_DO_REMOTE),
+            "FSCHAT_MODEL_WORKERS": json.dumps(FSCHAT_MODEL_WORKERS)
+        }
+
+        with open(os.path.join(src_dir, "configs/local_config.json"), "w") as f:
+            json.dump(update_json, f)
+
+        from start import start_main
+        from stop import stop_main
+        stop_main()
+        start_main()

tests/chains_test.py

@@ -79,7 +79,7 @@ print(src_dir)
 
 # chain的测试
 llm_config = LLMConfig(
-    model_name="gpt-3.5-turbo", model_device="cpu",api_key=os.environ["OPENAI_API_KEY"], 
+    model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"], 
     api_base_url=os.environ["API_BASE_URL"], temperature=0.3
     )
 embed_config = EmbedConfig(

tests/openai_test.py

@@ -5,7 +5,7 @@ src_dir = os.path.join(
 )
 sys.path.append(src_dir)
 
-from configs import llm_model_dict, LLM_MODEL
+from configs.model_config import llm_model_dict, LLM_MODEL
 import openai
 # os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:7890"
 # os.environ["OPENAI_PROXY"] = "http://127.0.0.1:7890"
@@ -22,30 +22,32 @@ if __name__ == "__main__":
     # chat = ChatOpenAI(temperature=0.1, model_name="gpt-3.5-turbo")
     # print(chat.predict("hi!"))
 
-    # print(LLM_MODEL, llm_model_dict[LLM_MODEL]["api_key"], llm_model_dict[LLM_MODEL]["api_base_url"])
+    print(LLM_MODEL, llm_model_dict[LLM_MODEL]["api_key"], llm_model_dict[LLM_MODEL]["api_base_url"])
-    # model = ChatOpenAI(
+    from langchain.chat_models import ChatOpenAI
-    #     streaming=True,
+    model = ChatOpenAI(
-    #     verbose=True,
+        streaming=True,
-    #     openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
+        verbose=True,
-    #     openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
+        openai_api_key="dsdadas",
-    #     model_name=LLM_MODEL
+        openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
-    # )
+        model_name=LLM_MODEL
+    )
+    print(model.predict("hi!"))
     # chat_prompt = ChatPromptTemplate.from_messages([("human", "{input}")])
     # chain = LLMChain(prompt=chat_prompt, llm=model)
     # content = chain({"input": "hello"})
     # print(content)
 
-    import openai
+    # import openai
-    # openai.api_key = "EMPTY" # Not support yet
+    # # openai.api_key = "EMPTY" # Not support yet
-    openai.api_base = "http://127.0.0.1:8888/v1"
+    # openai.api_base = "http://127.0.0.1:8888/v1"
 
-    model = "example"
+    # model = "example"
 
-    # create a chat completion
+    # # create a chat completion
-    completion = openai.ChatCompletion.create(
+    # completion = openai.ChatCompletion.create(
-    model=model,
+    # model=model,
-    messages=[{"role": "user", "content": "Hello! What is your name? "}],
+    # messages=[{"role": "user", "content": "Hello! What is your name? "}],
-    max_tokens=100,
+    # max_tokens=100,
-    )
+    # )
-    # print the completion
+    # # print the completion
-    print(completion.choices[0].message.content)
+    # print(completion.choices[0].message.content)

tests/sandbox_test.py

@@ -86,7 +86,8 @@ pycodebox = PyCodeBox(remote_url="http://localhost:5050",
 reuslt = pycodebox.chat("```import os\nos.getcwd()```", do_code_exe=True)
 print(reuslt)
 
-reuslt = pycodebox.chat("print('hello world!')", do_code_exe=False)
+# reuslt = pycodebox.chat("```print('hello world!')```", do_code_exe=True)
+reuslt = pycodebox.chat("print('hello world!')", do_code_exe=True)
 print(reuslt)