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## 🛠 新增功能 - 支持百川在线模型 (@hzg0601 @liunux4odoo in #1623) - 支持 Azure OpenAI 与 claude 等 Langchain 自带模型 (@zRzRzRzRzRzRzR in #1808) - Agent 功能大量更新，支持更多的工具、更换提示词、检索知识库 (@zRzRzRzRzRzRzR in #1626 #1666 #1785) - 加长 32k 模型的历史记录 (@zRzRzRzRzRzRzR in #1629 #1630) - *_chat 接口支持 max_tokens 参数 (@liunux4odoo in #1744) - 实现 API 和 WebUI 的前后端分离 (@liunux4odoo in #1772) - 支持 zlilliz 向量库 (@zRzRzRzRzRzRzR in #1785) - 支持 metaphor 搜索引擎 (@liunux4odoo in #1792) - 支持 p-tuning 模型 (@hzg0601 in #1810) - 更新完善文档和 Wiki (@imClumsyPanda @zRzRzRzRzRzRzR @glide-the in #1680 #1811) ## 🐞 问题修复 - 修复 bge-* 模型匹配超过 1 的问题 (@zRzRzRzRzRzRzR in #1652) - 修复系统代理为空的问题 (@glide-the in #1654) - 修复重建知识库时 `d == self.d assert error` (@liunux4odoo in #1766) - 修复对话历史消息错误 (@liunux4odoo in #1801) - 修复 OpenAI 无法调用的 bug (@zRzRzRzRzRzRzR in #1808) - 修复 windows下 BIND_HOST=0.0.0.0 时对话出错的问题 (@hzg0601 in #1810)
259 lines
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259 lines
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Plaintext
import os
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# 可以指定一个绝对路径，统一存放所有的Embedding和LLM模型。
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# 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
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MODEL_ROOT_PATH = ""
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# 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：
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# 1、将对应的值修改为模型绝对路径
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# 2、不修改此处的值（以 text2vec 为例）：
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# 2.1 如果{MODEL_ROOT_PATH}下存在如下任一子目录：
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# - text2vec
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# - GanymedeNil/text2vec-large-chinese
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# - text2vec-large-chinese
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# 2.2 如果以上本地路径不存在，则使用huggingface模型
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MODEL_PATH = {
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"embed_model": {
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"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
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"ernie-base": "nghuyong/ernie-3.0-base-zh",
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"text2vec-base": "shibing624/text2vec-base-chinese",
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"text2vec": "GanymedeNil/text2vec-large-chinese",
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"text2vec-paraphrase": "shibing624/text2vec-base-chinese-paraphrase",
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"text2vec-sentence": "shibing624/text2vec-base-chinese-sentence",
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"text2vec-multilingual": "shibing624/text2vec-base-multilingual",
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"text2vec-bge-large-chinese": "shibing624/text2vec-bge-large-chinese",
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"m3e-small": "moka-ai/m3e-small",
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"m3e-base": "moka-ai/m3e-base",
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"m3e-large": "moka-ai/m3e-large",
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"bge-small-zh": "BAAI/bge-small-zh",
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"bge-base-zh": "BAAI/bge-base-zh",
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"bge-large-zh": "BAAI/bge-large-zh",
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"bge-large-zh-noinstruct": "BAAI/bge-large-zh-noinstruct",
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"bge-base-zh-v1.5": "BAAI/bge-base-zh-v1.5",
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"bge-large-zh-v1.5": "BAAI/bge-large-zh-v1.5",
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"piccolo-base-zh": "sensenova/piccolo-base-zh",
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"piccolo-large-zh": "sensenova/piccolo-large-zh",
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"text-embedding-ada-002": "your OPENAI_API_KEY",
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},
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# TODO: add all supported llm models
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"llm_model": {
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# 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
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"chatglm-6b": "THUDM/chatglm-6b",
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"chatglm2-6b": "THUDM/chatglm2-6b",
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"chatglm2-6b-int4": "THUDM/chatglm2-6b-int4",
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"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
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"baichuan2-13b": "baichuan-inc/Baichuan2-13B-Chat",
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"baichuan2-7b":"baichuan-inc/Baichuan2-7B-Chat",
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"baichuan-7b": "baichuan-inc/Baichuan-7B",
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"baichuan-13b": "baichuan-inc/Baichuan-13B",
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'baichuan-13b-chat':'baichuan-inc/Baichuan-13B-Chat',
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"aquila-7b":"BAAI/Aquila-7B",
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"aquilachat-7b":"BAAI/AquilaChat-7B",
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"internlm-7b":"internlm/internlm-7b",
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"internlm-chat-7b":"internlm/internlm-chat-7b",
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"falcon-7b":"tiiuae/falcon-7b",
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"falcon-40b":"tiiuae/falcon-40b",
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"falcon-rw-7b":"tiiuae/falcon-rw-7b",
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"gpt2":"gpt2",
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"gpt2-xl":"gpt2-xl",
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"gpt-j-6b":"EleutherAI/gpt-j-6b",
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"gpt4all-j":"nomic-ai/gpt4all-j",
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"gpt-neox-20b":"EleutherAI/gpt-neox-20b",
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"pythia-12b":"EleutherAI/pythia-12b",
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"oasst-sft-4-pythia-12b-epoch-3.5":"OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
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"dolly-v2-12b":"databricks/dolly-v2-12b",
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"stablelm-tuned-alpha-7b":"stabilityai/stablelm-tuned-alpha-7b",
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"Llama-2-13b-hf":"meta-llama/Llama-2-13b-hf",
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"Llama-2-70b-hf":"meta-llama/Llama-2-70b-hf",
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"open_llama_13b":"openlm-research/open_llama_13b",
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"vicuna-13b-v1.3":"lmsys/vicuna-13b-v1.3",
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"koala":"young-geng/koala",
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"mpt-7b":"mosaicml/mpt-7b",
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"mpt-7b-storywriter":"mosaicml/mpt-7b-storywriter",
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"mpt-30b":"mosaicml/mpt-30b",
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"opt-66b":"facebook/opt-66b",
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"opt-iml-max-30b":"facebook/opt-iml-max-30b",
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"Qwen-7B":"Qwen/Qwen-7B",
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"Qwen-14B":"Qwen/Qwen-14B",
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"Qwen-7B-Chat":"Qwen/Qwen-7B-Chat",
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"Qwen-14B-Chat":"Qwen/Qwen-14B-Chat",
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},
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}
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# 选用的 Embedding 名称
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EMBEDDING_MODEL = "m3e-base" # 可以尝试最新的嵌入式sota模型：bge-large-zh-v1.5
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# 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。
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EMBEDDING_DEVICE = "auto"
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# LLM 名称
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LLM_MODEL = "chatglm2-6b"
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# 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。
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LLM_DEVICE = "auto"
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# 历史对话轮数
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HISTORY_LEN = 3
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# LLM通用对话参数
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TEMPERATURE = 0.7
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# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
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LANGCHAIN_LLM_MODEL = {
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# 不需要走Fschat封装的，Langchain直接支持的模型。
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# 调用chatgpt时如果报出： urllib3.exceptions.MaxRetryError: HTTPSConnectionPool(host='api.openai.com', port=443):
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# Max retries exceeded with url: /v1/chat/completions
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# 则需要将urllib3版本修改为1.25.11
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# 如果依然报urllib3.exceptions.MaxRetryError: HTTPSConnectionPool，则将https改为http
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# 参考https://zhuanlan.zhihu.com/p/350015032
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# 如果报出：raise NewConnectionError(
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# urllib3.exceptions.NewConnectionError: <urllib3.connection.HTTPSConnection object at 0x000001FE4BDB85E0>:
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# Failed to establish a new connection: [WinError 10060]
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# 则是因为内地和香港的IP都被OPENAI封了，需要切换为日本、新加坡等地
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# 如果出现WARNING: Retrying langchain.chat_models.openai.acompletion_with_retry.<locals>._completion_with_retry in
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# 4.0 seconds as it raised APIConnectionError: Error communicating with OpenAI.
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# 需要添加代理访问(正常开的代理软件可能会拦截不上)需要设置配置openai_proxy 或者 使用环境遍历OPENAI_PROXY 进行设置
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# 比如: "openai_proxy": 'http://127.0.0.1:4780'
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# 这些配置文件的名字不能改动
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"Azure-OpenAI": {
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"deployment_name": "your Azure deployment name",
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"model_version": "0701",
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"openai_api_type": "azure",
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"api_base_url": "https://your Azure point.azure.com",
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"api_version": "2023-07-01-preview",
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"api_key": "your Azure api key",
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"openai_proxy": "",
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},
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"OpenAI": {
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"model_name": "your openai model name(such as gpt-4)",
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"api_base_url": "https://api.openai.com/v1",
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"api_key": "your OPENAI_API_KEY",
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"openai_proxy": "",
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},
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"Anthropic": {
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"model_name": "your claude model name(such as claude2-100k)",
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"api_key":"your ANTHROPIC_API_KEY",
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}
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}
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ONLINE_LLM_MODEL = {
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# 线上模型。请在server_config中为每个在线API设置不同的端口
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# 具体注册及api key获取请前往 http://open.bigmodel.cn
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"zhipu-api": {
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"api_key": "",
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"version": "chatglm_pro", # 可选包括 "chatglm_lite", "chatglm_std", "chatglm_pro"
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"provider": "ChatGLMWorker",
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},
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# 具体注册及api key获取请前往 https://api.minimax.chat/
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"minimax-api": {
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"group_id": "",
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"api_key": "",
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"is_pro": False,
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"provider": "MiniMaxWorker",
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},
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# 具体注册及api key获取请前往 https://xinghuo.xfyun.cn/
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"xinghuo-api": {
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"APPID": "",
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"APISecret": "",
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"api_key": "",
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"is_v2": False,
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"provider": "XingHuoWorker",
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},
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# 百度千帆 API，申请方式请参考 https://cloud.baidu.com/doc/WENXINWORKSHOP/s/4lilb2lpf
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"qianfan-api": {
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"version": "ernie-bot-turbo", # 当前支持 "ernie-bot" 或 "ernie-bot-turbo"， 更多的见官方文档。
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"version_url": "", # 也可以不填写version，直接填写在千帆申请模型发布的API地址
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"api_key": "",
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"secret_key": "",
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"provider": "QianFanWorker",
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},
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# 火山方舟 API，文档参考 https://www.volcengine.com/docs/82379
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"fangzhou-api": {
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"version": "chatglm-6b-model", # 当前支持 "chatglm-6b-model"， 更多的见文档模型支持列表中方舟部分。
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"version_url": "", # 可以不填写version，直接填写在方舟申请模型发布的API地址
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"api_key": "",
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"secret_key": "",
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"provider": "FangZhouWorker",
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},
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# 阿里云通义千问 API，文档参考 https://help.aliyun.com/zh/dashscope/developer-reference/api-details
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"qwen-api": {
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"version": "qwen-turbo", # 可选包括 "qwen-turbo", "qwen-plus"
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"api_key": "", # 请在阿里云控制台模型服务灵积API-KEY管理页面创建
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"provider": "QwenWorker",
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},
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# 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter
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"baichuan-api": {
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"version": "Baichuan2-53B", # 当前支持 "Baichuan2-53B"， 见官方文档。
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"api_key": "",
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"secret_key": "",
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"provider": "BaiChuanWorker",
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},
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}
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# 通常情况下不需要更改以下内容
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# nltk 模型存储路径
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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VLLM_MODEL_DICT = {
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"aquila-7b":"BAAI/Aquila-7B",
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"aquilachat-7b":"BAAI/AquilaChat-7B",
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"baichuan-7b": "baichuan-inc/Baichuan-7B",
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"baichuan-13b": "baichuan-inc/Baichuan-13B",
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'baichuan-13b-chat':'baichuan-inc/Baichuan-13B-Chat',
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# 注意：bloom系列的tokenizer与model是分离的，因此虽然vllm支持，但与fschat框架不兼容
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# "bloom":"bigscience/bloom",
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# "bloomz":"bigscience/bloomz",
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# "bloomz-560m":"bigscience/bloomz-560m",
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# "bloomz-7b1":"bigscience/bloomz-7b1",
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# "bloomz-1b7":"bigscience/bloomz-1b7",
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"internlm-7b":"internlm/internlm-7b",
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"internlm-chat-7b":"internlm/internlm-chat-7b",
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"falcon-7b":"tiiuae/falcon-7b",
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"falcon-40b":"tiiuae/falcon-40b",
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"falcon-rw-7b":"tiiuae/falcon-rw-7b",
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"gpt2":"gpt2",
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"gpt2-xl":"gpt2-xl",
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"gpt-j-6b":"EleutherAI/gpt-j-6b",
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"gpt4all-j":"nomic-ai/gpt4all-j",
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"gpt-neox-20b":"EleutherAI/gpt-neox-20b",
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"pythia-12b":"EleutherAI/pythia-12b",
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"oasst-sft-4-pythia-12b-epoch-3.5":"OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
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"dolly-v2-12b":"databricks/dolly-v2-12b",
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"stablelm-tuned-alpha-7b":"stabilityai/stablelm-tuned-alpha-7b",
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"Llama-2-13b-hf":"meta-llama/Llama-2-13b-hf",
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"Llama-2-70b-hf":"meta-llama/Llama-2-70b-hf",
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"open_llama_13b":"openlm-research/open_llama_13b",
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"vicuna-13b-v1.3":"lmsys/vicuna-13b-v1.3",
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"koala":"young-geng/koala",
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"mpt-7b":"mosaicml/mpt-7b",
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"mpt-7b-storywriter":"mosaicml/mpt-7b-storywriter",
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"mpt-30b":"mosaicml/mpt-30b",
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"opt-66b":"facebook/opt-66b",
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"opt-iml-max-30b":"facebook/opt-iml-max-30b",
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"Qwen-7B":"Qwen/Qwen-7B",
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"Qwen-14B":"Qwen/Qwen-14B",
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"Qwen-7B-Chat":"Qwen/Qwen-7B-Chat",
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"Qwen-14B-Chat":"Qwen/Qwen-14B-Chat",
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} |