支持通过配置项同时启动多个模型，将Wiki纳入samples知识库

.gitignore

@@ -2,7 +2,10 @@
 *.log.*
 *.bak
 logs
-/knowledge_base/
+/knowledge_base/*
+!/knowledge_base/samples
+/knowledge_base/samples/vector_store
+
 /configs/*.py
 .vscode/
 

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "knowledge_base/samples/content/wiki"]
+	path = knowledge_base/samples/content/wiki
+	url = https://github.com/chatchat-space/Langchain-Chatchat.wiki.git

chains/llmchain_with_history.py

@@ -1,12 +1,12 @@
 from server.utils import get_ChatOpenAI
-from configs.model_config import LLM_MODEL, TEMPERATURE
+from configs.model_config import LLM_MODELS, TEMPERATURE
 from langchain.chains import LLMChain
 from langchain.prompts.chat import (
     ChatPromptTemplate,
     HumanMessagePromptTemplate,
 )
 
-model = get_ChatOpenAI(model_name=LLM_MODEL, temperature=TEMPERATURE)
+model = get_ChatOpenAI(model_name=LLM_MODELS[0], temperature=TEMPERATURE)
 
 
 human_prompt = "{input}"

configs/basic_config.py.example

@@ -2,6 +2,7 @@ import logging
 import os
 import langchain
 
+
 # 是否显示详细日志
 log_verbose = False
 langchain.verbose = False

configs/kb_config.py.example

@@ -56,7 +56,10 @@ KB_INFO = {
     "知识库名称": "知识库介绍",
     "samples": "关于本项目issue的解答",
 }
+
+
 # 通常情况下不需要更改以下内容
+
 # 知识库默认存储路径
 KB_ROOT_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "knowledge_base")
 if not os.path.exists(KB_ROOT_PATH):

configs/model_config.py.example

@@ -1,96 +1,13 @@
 import os
 
+
 # 可以指定一个绝对路径，统一存放所有的Embedding和LLM模型。
-# 每个模型可以是一个单独的目录，也可以是某个目录下的二级子目录
+# 每个模型可以是一个单独的目录，也可以是某个目录下的二级子目录。
+# 如果模型目录名称和 MODEL_PATH 中的 key 或 value 相同，程序会自动检测加载，无需修改 MODEL_PATH 中的路径。
 MODEL_ROOT_PATH = ""
 
-# 在以下字典中修改属性值，以指定本地embedding模型存储位置。支持3种设置方法：
-# 1、将对应的值修改为模型绝对路径
-# 2、不修改此处的值（以 text2vec 为例）：
-#       2.1 如果{MODEL_ROOT_PATH}下存在如下任一子目录：
-#           - text2vec
-#           - GanymedeNil/text2vec-large-chinese
-#           - text2vec-large-chinese
-#       2.2 如果以上本地路径不存在，则使用huggingface模型
-MODEL_PATH = {
-    "embed_model": {
-        "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
-        "ernie-base": "nghuyong/ernie-3.0-base-zh",
-        "text2vec-base": "shibing624/text2vec-base-chinese",
-        "text2vec": "GanymedeNil/text2vec-large-chinese",
-        "text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
-        "text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
-        "text2vec-multilingual": "shibing624/text2vec-base-multilingual",
-        "text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese",
-        "m3e-small": "moka-ai/m3e-small",
-        "m3e-base": "moka-ai/m3e-base",
-        "m3e-large": "moka-ai/m3e-large",
-        "bge-small-zh": "BAAI/bge-small-zh",
-        "bge-base-zh": "BAAI/bge-base-zh",
-        "bge-large-zh": "BAAI/bge-large-zh",
-        "bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
-        "bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
-        "bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",
-        "piccolo-base-zh": "sensenova/piccolo-base-zh",
-        "piccolo-large-zh": "sensenova/piccolo-large-zh",
-        "text-embedding-ada-002": "your OPENAI_API_KEY",
-    },
-    # TODO: add all supported llm models
-    "llm_model": {
-        # 以下部分模型并未完全测试，仅根据fastchat和vllm模型的模型列表推定支持
-        "chatglm2-6b": "THUDM/chatglm2-6b",
-        "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
-        "chatglm3-6b": "THUDM/chatglm3-6b-32k",
-        "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",
-
-        "baichuan2-13b": "baichuan-inc/Baichuan2-13B-Chat",
-        "baichuan2-7b": "baichuan-inc/Baichuan2-7B-Chat",
-
-        "baichuan-7b": "baichuan-inc/Baichuan-7B",
-        "baichuan-13b": "baichuan-inc/Baichuan-13B",
-        'baichuan-13b-chat': 'baichuan-inc/Baichuan-13B-Chat',
-
-        "aquila-7b": "BAAI/Aquila-7B",
-        "aquilachat-7b": "BAAI/AquilaChat-7B",
-
-        "internlm-7b": "internlm/internlm-7b",
-        "internlm-chat-7b": "internlm/internlm-chat-7b",
-
-        "falcon-7b": "tiiuae/falcon-7b",
-        "falcon-40b": "tiiuae/falcon-40b",
-        "falcon-rw-7b": "tiiuae/falcon-rw-7b",
-
-        "gpt2": "gpt2",
-        "gpt2-xl": "gpt2-xl",
-
-        "gpt-j-6b": "EleutherAI/gpt-j-6b",
-        "gpt4all-j": "nomic-ai/gpt4all-j",
-        "gpt-neox-20b": "EleutherAI/gpt-neox-20b",
-        "pythia-12b": "EleutherAI/pythia-12b",
-        "oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
-        "dolly-v2-12b": "databricks/dolly-v2-12b",
-        "stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b",
-
-        "Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf",
-        "Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf",
-        "open_llama_13b": "openlm-research/open_llama_13b",
-        "vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3",
-        "koala": "young-geng/koala",
-
-        "mpt-7b": "mosaicml/mpt-7b",
-        "mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter",
-        "mpt-30b": "mosaicml/mpt-30b",
-        "opt-66b": "facebook/opt-66b",
-        "opt-iml-max-30b": "facebook/opt-iml-max-30b",
-
-        "Qwen-7B": "Qwen/Qwen-7B",
-        "Qwen-14B": "Qwen/Qwen-14B",
-        "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
-        "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
-    },
-}
 # 选用的 Embedding 名称
-EMBEDDING_MODEL = "m3e-base"  # 可以尝试最新的嵌入式sota模型：bge-large-zh-v1.5
+EMBEDDING_MODEL = "m3e-base" # bge-large-zh
 
 # Embedding 模型运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
 EMBEDDING_DEVICE = "auto"
@@ -99,9 +16,11 @@ EMBEDDING_DEVICE = "auto"
 EMBEDDING_KEYWORD_FILE = "keywords.txt"
 EMBEDDING_MODEL_OUTPUT_PATH = "output"
 
-# LLM 名称
-LLM_MODEL = "chatglm2-6b"
-# AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODEL)
+# 要运行的 LLM 名称，可以包括本地模型和在线模型。
+# 第一个将作为 API 和 WEBUI 的默认模型
+LLM_MODELS = ["chatglm2-6b-int4", "zhipu-api", "openai-api]
+
+# AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])
 Agent_MODEL = None
 
 # LLM 运行设备。设为"auto"会自动检测，也可手动设定为"cuda","mps","cpu"其中之一。
@@ -111,7 +30,6 @@ LLM_DEVICE = "auto"
 HISTORY_LEN = 3
 
 # 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度
-
 MAX_TOKENS = None
 
 # LLM通用对话参数
@@ -197,6 +115,93 @@ ONLINE_LLM_MODEL = {
     },
 }
 
+# 在以下字典中修改属性值，以指定本地embedding模型存储位置。支持3种设置方法：
+# 1、将对应的值修改为模型绝对路径
+# 2、不修改此处的值（以 text2vec 为例）：
+#       2.1 如果{MODEL_ROOT_PATH}下存在如下任一子目录：
+#           - text2vec
+#           - GanymedeNil/text2vec-large-chinese
+#           - text2vec-large-chinese
+#       2.2 如果以上本地路径不存在，则使用huggingface模型
+MODEL_PATH = {
+    "embed_model": {
+        "ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
+        "ernie-base": "nghuyong/ernie-3.0-base-zh",
+        "text2vec-base": "shibing624/text2vec-base-chinese",
+        "text2vec": "GanymedeNil/text2vec-large-chinese",
+        "text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
+        "text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
+        "text2vec-multilingual": "shibing624/text2vec-base-multilingual",
+        "text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese",
+        "m3e-small": "moka-ai/m3e-small",
+        "m3e-base": "moka-ai/m3e-base",
+        "m3e-large": "moka-ai/m3e-large",
+        "bge-small-zh": "BAAI/bge-small-zh",
+        "bge-base-zh": "BAAI/bge-base-zh",
+        "bge-large-zh": "BAAI/bge-large-zh",
+        "bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
+        "bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
+        "bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",
+        "piccolo-base-zh": "sensenova/piccolo-base-zh",
+        "piccolo-large-zh": "sensenova/piccolo-large-zh",
+        "text-embedding-ada-002": "your OPENAI_API_KEY",
+    },
+
+    "llm_model": {
+        # 以下部分模型并未完全测试，仅根据fastchat和vllm模型的模型列表推定支持
+        "chatglm2-6b": "THUDM/chatglm2-6b",
+        "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
+        "chatglm3-6b": "THUDM/chatglm3-6b-32k",
+        "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",
+
+        "baichuan2-13b": "baichuan-inc/Baichuan2-13B-Chat",
+        "baichuan2-7b": "baichuan-inc/Baichuan2-7B-Chat",
+
+        "baichuan-7b": "baichuan-inc/Baichuan-7B",
+        "baichuan-13b": "baichuan-inc/Baichuan-13B",
+        'baichuan-13b-chat': 'baichuan-inc/Baichuan-13B-Chat',
+
+        "aquila-7b": "BAAI/Aquila-7B",
+        "aquilachat-7b": "BAAI/AquilaChat-7B",
+
+        "internlm-7b": "internlm/internlm-7b",
+        "internlm-chat-7b": "internlm/internlm-chat-7b",
+
+        "falcon-7b": "tiiuae/falcon-7b",
+        "falcon-40b": "tiiuae/falcon-40b",
+        "falcon-rw-7b": "tiiuae/falcon-rw-7b",
+
+        "gpt2": "gpt2",
+        "gpt2-xl": "gpt2-xl",
+
+        "gpt-j-6b": "EleutherAI/gpt-j-6b",
+        "gpt4all-j": "nomic-ai/gpt4all-j",
+        "gpt-neox-20b": "EleutherAI/gpt-neox-20b",
+        "pythia-12b": "EleutherAI/pythia-12b",
+        "oasst-sft-4-pythia-12b-epoch-3.5": "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
+        "dolly-v2-12b": "databricks/dolly-v2-12b",
+        "stablelm-tuned-alpha-7b": "stabilityai/stablelm-tuned-alpha-7b",
+
+        "Llama-2-13b-hf": "meta-llama/Llama-2-13b-hf",
+        "Llama-2-70b-hf": "meta-llama/Llama-2-70b-hf",
+        "open_llama_13b": "openlm-research/open_llama_13b",
+        "vicuna-13b-v1.3": "lmsys/vicuna-13b-v1.3",
+        "koala": "young-geng/koala",
+
+        "mpt-7b": "mosaicml/mpt-7b",
+        "mpt-7b-storywriter": "mosaicml/mpt-7b-storywriter",
+        "mpt-30b": "mosaicml/mpt-30b",
+        "opt-66b": "facebook/opt-66b",
+        "opt-iml-max-30b": "facebook/opt-iml-max-30b",
+
+        "Qwen-7B": "Qwen/Qwen-7B",
+        "Qwen-14B": "Qwen/Qwen-14B",
+        "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
+        "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
+    },
+}
+
+
 # 通常情况下不需要更改以下内容
 
 # nltk 模型存储路径

configs/server_config.py.example

@@ -31,8 +31,7 @@ FSCHAT_OPENAI_API = {
 
 # fastchat model_worker server
 # 这些模型必须是在model_config.MODEL_PATH或ONLINE_MODEL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
-# 必须在这里添加的模型才会出现在WEBUI中可选模型列表里（LLM_MODEL会自动添加）
+# 在启动startup.py时，可用通过`--model-name xxxx yyyy`指定模型，不指定则为LLM_MODELS
 FSCHAT_MODEL_WORKERS = {
     # 所有模型共用的默认配置，可在模型专项配置中进行覆盖。
     "default": {
@@ -58,7 +57,7 @@ FSCHAT_MODEL_WORKERS = {
         # "awq_ckpt": None,
         # "awq_wbits": 16,
         # "awq_groupsize": -1,
-        # "model_names": [LLM_MODEL],
+        # "model_names": LLM_MODELS,
         # "conv_template": None,
         # "limit_worker_concurrency": 5,
         # "stream_interval": 2,
@@ -96,30 +95,31 @@ FSCHAT_MODEL_WORKERS = {
     #    "device": "cpu",
     # },
 
-    "zhipu-api": { # 请为每个要运行的在线API设置不同的端口
+    #以下配置可以不用修改，在model_config中设置启动的模型
+    "zhipu-api": {
         "port": 21001,
     },
-    # "minimax-api": {
-    #     "port": 21002,
-    # },
-    # "xinghuo-api": {
-    #     "port": 21003,
-    # },
-    # "qianfan-api": {
-    #     "port": 21004,
-    # },
-    # "fangzhou-api": {
-    #     "port": 21005,
-    # },
-    # "qwen-api": {
-    #     "port": 21006,
-    # },
-    # "baichuan-api": {
-    #     "port": 21007,
-    # },
-    # "azure-api": {
-    #    "port": 21008,
-    # },
+    "minimax-api": {
+        "port": 21002,
+    },
+    "xinghuo-api": {
+        "port": 21003,
+    },
+    "qianfan-api": {
+        "port": 21004,
+    },
+    "fangzhou-api": {
+        "port": 21005,
+    },
+    "qwen-api": {
+        "port": 21006,
+    },
+    "baichuan-api": {
+        "port": 21007,
+    },
+    "azure-api": {
+        "port": 21008,
+    },
 }
 
 # fastchat multi model worker server

requirements.txt

@@ -53,7 +53,7 @@ vllm>=0.2.0; sys_platform == "linux"
 
 # WebUI requirements
 
-streamlit>=1.26.0
+streamlit~=1.27.0
 streamlit-option-menu>=0.3.6
 streamlit-antd-components>=0.1.11
 streamlit-chatbox>=1.1.11

requirements_lite.txt

@@ -41,7 +41,7 @@ dashscope>=1.10.0 # qwen
 
 numpy~=1.24.4
 pandas~=2.0.3
-streamlit>=1.26.0
+streamlit~=1.27.0
 streamlit-option-menu>=0.3.6
 streamlit-antd-components>=0.1.11
 streamlit-chatbox==1.1.11

requirements_webui.txt

@@ -1,6 +1,6 @@
 # WebUI requirements
 
-streamlit>=1.26.0
+streamlit~=1.27.0
 streamlit-option-menu>=0.3.6
 streamlit-antd-components>=0.1.11
 streamlit-chatbox>=1.1.11

server/chat/agent_chat.py

@@ -5,7 +5,7 @@
 from server.agent.custom_template import CustomOutputParser, CustomPromptTemplate
 from fastapi import Body
 from fastapi.responses import StreamingResponse
-from configs import LLM_MODEL, TEMPERATURE, HISTORY_LEN, Agent_MODEL
+from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN, Agent_MODEL
 from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
 from langchain.chains import LLMChain
 from typing import AsyncIterable, Optional, Dict
@@ -26,7 +26,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
                                                         "content": "使用天气查询工具查询到今天北京多云，10-14摄氏度，东北风2级，易感冒"}]]
                                                    ),
                      stream: bool = Body(False, description="流式输出"),
-                     model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
+                     model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
                      temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
                      max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"),
                      prompt_name: str = Body("default",
@@ -38,7 +38,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
     async def agent_chat_iterator(
             query: str,
             history: Optional[List[History]],
-            model_name: str = LLM_MODEL,
+            model_name: str = LLM_MODELS[0],
             prompt_name: str = prompt_name,
     ) -> AsyncIterable[str]:
         callback = CustomAsyncIteratorCallbackHandler()