update model server features from langchain-chatchat

configs/model_config.py.example

@@ -51,6 +51,17 @@ EMBEDDING_MODEL = "text2vec-base"
 # 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 = {
+    # 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_key": "",
+        "openai_proxy": "",
+    },
+}
+
 
 llm_model_dict = {
     "chatglm-6b": {
@@ -123,6 +134,7 @@ llm_model_dict = llm_model_dict_c
 
 # LLM 名称
 LLM_MODEL = "gpt-3.5-turbo"
+LLM_MODELs = ["chatglm2-6b"]
 USE_FASTCHAT = "gpt" not in LLM_MODEL # 判断是否进行fastchat
 
 # LLM 运行设备

configs/server_config.py.example

@@ -76,7 +76,7 @@ SANDBOX_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 = {
-    LLM_MODEL: {
+    "default": {
         "host": DEFAULT_BIND_HOST,
         "port": 20002,
         "device": LLM_DEVICE,
@@ -100,8 +100,13 @@ FSCHAT_MODEL_WORKERS = {
         # "stream_interval": 2,
         # "no_register": False,
     },
+    "chatglm2-6b": {
+        "port": 20003
+    },
+    "baichuan2-7b-base": {
+        "port": 20004
+    }
 }
-
 # fastchat multi model worker server
 FSCHAT_MULTI_MODEL_WORKERS = {
     # todo

examples/agent_examples/codeChatXX_examply.py

@@ -0,0 +1,59 @@
+import os, sys, requests
+
+src_dir = os.path.join(
+    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+)
+sys.path.append(src_dir)
+
+from dev_opsgpt.tools import (
+    toLangchainTools, get_tool_schema, DDGSTool, DocRetrieval,
+    TOOL_DICT, TOOL_SETS
+    )
+
+from configs.model_config import *
+from dev_opsgpt.connector.phase import BasePhase
+from dev_opsgpt.connector.agents import BaseAgent
+from dev_opsgpt.connector.chains import BaseChain
+from dev_opsgpt.connector.schema import (
+    Message, Memory, load_role_configs, load_phase_configs, load_chain_configs
+    )
+from dev_opsgpt.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS
+import importlib
+
+tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
+
+
+role_configs = load_role_configs(AGETN_CONFIGS)
+chain_configs = load_chain_configs(CHAIN_CONFIGS)
+phase_configs = load_phase_configs(PHASE_CONFIGS)
+
+agent_module = importlib.import_module("dev_opsgpt.connector.agents")
+
+
+phase_name = "codeChatPhase"
+phase = BasePhase(phase_name,
+            task = None,
+            phase_config = PHASE_CONFIGS,
+            chain_config = CHAIN_CONFIGS,
+            role_config = AGETN_CONFIGS,
+            do_summary=False,
+            do_code_retrieval=True,
+            do_doc_retrieval=False,
+            do_search=False,
+            )
+
+# 代码一共有多少类 => 基于cypher
+# 代码库里有哪些函数，返回5个就行 => 基于cypher
+# remove 这个函数是做什么的  => 基于标签
+# 有没有函数已经实现了从字符串删除指定字符串的功能，使用的话可以怎么使用，写个java代码  => 基于描述
+# 有根据我以下的需求用 java 开发一个方法：输入为字符串，将输入中的 .java 字符串给删除掉，然后返回新的字符串 => 基于描述
+
+# round-1
+query_content = "代码一共有多少类"
+query = Message(
+    role_name="user", role_type="human", 
+    role_content=query_content, input_query=query_content, origin_query=query_content,
+    code_engine_name="client", score_threshold=1.0, top_k=3, cb_search_type="cypher"
+    )
+
+output_message1, _ = phase.step(query)

requirements.txt

@@ -1,7 +1,7 @@
 langchain==0.0.266
 openai==0.28.1
 sentence_transformers
-fschat==0.2.24
+fschat==0.2.33
 transformers>=4.31.0
 # torch~=2.0.0
 fastapi~=0.99.1
@@ -45,3 +45,10 @@ javalang==0.13.0
 jsonref==1.1.0
 chromadb==0.4.17
 nebula3-python==3.1.0
+
+
+# qwen model
+protobuf==3.20.*
+transformers_stream_generator
+einops
+