mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-27 01:03:35 +08:00
对其ChatGLM3-6B与Qwen-14B
This commit is contained in:
zR
liunux4odoo
parent
d6e91e6638
commit
6f04e15aed
@ -1,8 +1,8 @@
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from .basic_config import *
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from .model_config import *
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from .kb_config import *
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from .server_config import *
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from .prompt_config import *
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VERSION = "v0.3.0-preview"
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from .basic_config import *
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from .model_config import *
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from .kb_config import *
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from .server_config import *
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from .prompt_config import *
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VERSION = "v0.3.0-preview"
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@ -25,8 +25,6 @@ if not os.path.exists(LOG_PATH):
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# 临时文件目录，主要用于文件对话
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BASE_TEMP_DIR = os.path.join(tempfile.gettempdir(), "chatchat")
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try:
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if os.path.isdir(BASE_TEMP_DIR):
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shutil.rmtree(BASE_TEMP_DIR)
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except Exception:
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pass
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os.makedirs(BASE_TEMP_DIR, exist_ok=True)
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@ -3,7 +3,7 @@ import os
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# 默认使用的知识库
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DEFAULT_KNOWLEDGE_BASE = "samples"
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# 默认向量库/全文检索引擎类型。可选：faiss, milvus(离线) & zilliz(在线), pgvector, chromadb 全文检索引擎es
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# 默认向量库/全文检索引擎类型。可选：faiss, milvus(离线) & zilliz(在线), pgvector,全文检索引擎es
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DEFAULT_VS_TYPE = "faiss"
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# 缓存向量库数量（针对FAISS）
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@ -21,44 +21,14 @@ OVERLAP_SIZE = 50
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# 知识库匹配向量数量
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VECTOR_SEARCH_TOP_K = 3
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# 知识库匹配的距离阈值，一般取值范围在0-1之间，SCORE越小，距离越小从而相关度越高。
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# 但有用户报告遇到过匹配分值超过1的情况，为了兼容性默认设为1，在WEBUI中调整范围为0-2
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SCORE_THRESHOLD = 1.0
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# 默认搜索引擎。可选：bing, duckduckgo, metaphor
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DEFAULT_SEARCH_ENGINE = "duckduckgo"
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# 搜索引擎匹配结题数量
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SEARCH_ENGINE_TOP_K = 3
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# Bing 搜索必备变量
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# 使用 Bing 搜索需要使用 Bing Subscription Key,需要在azure port中申请试用bing search
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# 具体申请方式请见
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# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/create-bing-search-service-resource
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# 使用python创建bing api 搜索实例详见:
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# https://learn.microsoft.com/en-us/bing/search-apis/bing-web-search/quickstarts/rest/python
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BING_SEARCH_URL = "https://api.bing.microsoft.com/v7.0/search"
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# 注意不是bing Webmaster Tools的api key，
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# 此外，如果是在服务器上，报Failed to establish a new connection: [Errno 110] Connection timed out
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# 是因为服务器加了防火墙，需要联系管理员加白名单，如果公司的服务器的话，就别想了GG
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BING_SUBSCRIPTION_KEY = ""
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# metaphor搜索需要KEY
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METAPHOR_API_KEY = ""
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# 心知天气 API KEY，用于天气Agent。申请：https://www.seniverse.com/
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SENIVERSE_API_KEY = ""
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# 知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右
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SCORE_THRESHOLD = 1
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# 是否开启中文标题加强，以及标题增强的相关配置
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# 通过增加标题判断，判断哪些文本为标题，并在metadata中进行标记；
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# 然后将文本与往上一级的标题进行拼合，实现文本信息的增强。
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ZH_TITLE_ENHANCE = False
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# PDF OCR 控制：只对宽高超过页面一定比例（图片宽/页面宽，图片高/页面高）的图片进行 OCR。
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# 这样可以避免 PDF 中一些小图片的干扰，提高非扫描版 PDF 处理速度
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PDF_OCR_THRESHOLD = (0.6, 0.6)
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# 每个知识库的初始化介绍，用于在初始化知识库时显示和Agent调用，没写则没有介绍，不会被Agent调用。
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KB_INFO = {
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@ -106,12 +76,7 @@ kbs_config = {
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"index_name": "test_index",
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"user": "",
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"password": ""
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},
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"milvus_kwargs":{
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"search_params":{"metric_type": "L2"}, #在此处增加search_params
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"index_params":{"metric_type": "L2","index_type": "HNSW"} # 在此处增加index_params
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},
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"chromadb": {}
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}
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}
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# TextSplitter配置项，如果你不明白其中的含义，就不要修改。
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@ -1,4 +1,5 @@
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import os
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MODEL_ROOT_PATH = ""
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EMBEDDING_MODEL = "bge-large-zh-v1.5" # bge-large-zh
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EMBEDDING_DEVICE = "auto"
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@ -12,32 +13,24 @@ RERANKER_MAX_LENGTH = 1024
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# 如果需要在 EMBEDDING_MODEL 中增加自定义的关键字时配置
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EMBEDDING_KEYWORD_FILE = "keywords.txt"
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EMBEDDING_MODEL_OUTPUT_PATH = "output"
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SUPPORT_AGENT_MODELS = [
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"chatglm3-6b",
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"Qwen-14b-Chat",
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"Qwen-1_8B-Chat",
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"openai-api"
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"openai-api",
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"Qwen-14B-Chat",
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"Qwen-7B-Chat",
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]
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LLM_MODEL_CONFIG = {
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# 意图识别不需要输出，模型后台知道就行
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"preprocess_model": {
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"zhipu-api": {
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"temperature": 0.4,
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"max_tokens": 2048,
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"history_len": 100,
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"chatglm3-6b": {
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"temperature": 0.01,
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"max_tokens": 5,
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"prompt_name": "default",
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"callbacks": False
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},
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},
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"llm_model": {
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"chatglm3-6b": {
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"temperature": 0.9,
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"max_tokens": 4096,
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"history_len": 3,
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"prompt_name": "default",
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"callbacks": True
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},
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"zhipu-api": {
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"temperature": 0.9,
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"max_tokens": 4000,
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"history_len": 5,
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@ -47,111 +40,28 @@ LLM_MODEL_CONFIG = {
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},
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"action_model": {
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"chatglm3-6b": {
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"temperature": 0.01,
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"temperature": 0.05,
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"max_tokens": 4096,
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"prompt_name": "ChatGLM3",
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"callbacks": True
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},
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"openai-api": {
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"temperature": 0.01,
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"max_tokens": 4096,
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"prompt_name": "GPT-4",
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"callbacks": True
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},
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},
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"postprocess_model": {
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"chatglm3-6b": {
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"zhipu-api": {
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"temperature": 0.01,
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"max_tokens": 4096,
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"prompt_name": "default",
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"callbacks": True
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}
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},
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}
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TOOL_CONFIG = {
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"search_local_knowledgebase": {
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"use": True,
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"top_k": 10,
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"score_threshold": 1,
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"conclude_prompt": {
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"with_result":
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'<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 "根据已知信息无法回答该问题"，'
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'不允许在答案中添加编造成分，答案请使用中文。 </指令>\n'
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'<已知信息>{{ context }}</已知信息>\n'
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'<问题>{{ question }}</问题>\n',
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"without_result":
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'请你根据我的提问回答我的问题:\n'
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'{{ question }}\n'
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'请注意，你必须在回答结束后强调，你的回答是根据你的经验回答而不是参考资料回答的。\n',
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}
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},
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"search_internet": {
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"use": True,
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"search_engine_name": "bing",
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"search_engine_config":
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{
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"bing": {
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"result_len": 3,
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"bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
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"bing_key": "",
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},
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"metaphor": {
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"result_len": 3,
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"metaphor_api_key": "",
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"split_result": False,
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"chunk_size": 500,
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"chunk_overlap": 0,
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},
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"duckduckgo": {
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"result_len": 3
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}
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},
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"top_k": 10,
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"verbose": "Origin",
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"conclude_prompt":
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"<指令>这是搜索到的互联网信息，请你根据这些信息进行提取并有调理，简洁的回答问题。如果无法从中得到答案，请说 “无法搜索到能回答问题的内容”。 "
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"</指令>\n<已知信息>{{ context }}</已知信息>\n"
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"<问题>\n"
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"{{ question }}\n"
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"</问题>\n"
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},
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"arxiv": {
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"use": True,
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},
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"shell": {
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"use": True,
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},
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"weather_check": {
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"use": True,
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"api-key": "",
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},
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"search_youtube": {
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"use": False,
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},
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"wolfram": {
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"use": False,
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},
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"calculate": {
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"use": False,
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},
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"aqa_processor": {
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"use": False,
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},
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"vqa_processor": {
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"use": False,
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},
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}
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# LLM 模型运行设备。设为"auto"会自动检测(会有警告)，也可手动设定为 "cuda","mps","cpu","xpu" 其中之一。
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LLM_DEVICE = "auto"
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ONLINE_LLM_MODEL = {
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"openai-api": {
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"model_name": "gpt-4-1106-preview",
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"api_base_url": "https://api.openai.com/v1",
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"api_key": "",
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"api_key": "sk-",
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"openai_proxy": "",
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},
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"zhipu-api": {
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@ -249,24 +159,31 @@ MODEL_PATH = {
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"bge-large-zh": "/media/zr/Data/Models/Embedding/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": "/Models/bge-large-zh-v1.5",
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"bge-large-zh-v1.5": "/share/home/zyx/Models/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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"nlp_gte_sentence-embedding_chinese-large": "/Models/nlp_gte_sentence-embedding_chinese-large",
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"text-embedding-ada-002": "Just write your OpenAI key like "sk-o3IGBhC9g8AiFvTGWVKsT*****" ",
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"text-embedding-ada-002": "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",
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},
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"llm_model": {
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"vicuna-7b-v1.5": "/share/official_pretrains/hf_home/vicuna-7b-v1.5",
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"Mixtral-8x7B-v0.1": "/share/home/zyx/Models/Mixtral-8x7B-v0.1",
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"chatglm2-6b": "THUDM/chatglm2-6b",
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"chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
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"chatglm3-6b": "/Models/chatglm3-6b",
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"chatglm3-6b": "/share/home/zyx/Models/chatglm3-6b",
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"chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",
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"Yi-34B-Chat": "/data/share/models/Yi-34B-Chat",
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"Yi-34B-Chat": "/share/home/zyx/Models/Yi-34B-Chat",
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"BlueLM-7B-Chat": "/Models/BlueLM-7B-Chat",
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"baichuan2-13b": "/media/zr/Data/Models/LLM/Baichuan2-13B-Chat",
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"baichuan2-7b": "/media/zr/Data/Models/LLM/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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@ -324,13 +241,13 @@ MODEL_PATH = {
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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-1_8B-Chat":"Qwen/Qwen-1_8B-Chat"
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"Qwen-1_8B-Chat": "/home/zr/Models/Qwen-1_8B-Chat",
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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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"Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8", # 确保已经安装了auto-gptq optimum flash-attn
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"Qwen-14B-Chat-Int4": "/media/zr/Data/Models/LLM/Qwen-14B-Chat-Int4", # 确保已经安装了auto-gptq optimum flash-attn
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"Qwen-14B-Chat": "/share/home/zyx/Models/Qwen-14B-Chat",
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"Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8",
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"Qwen-14B-Chat-Int4": "/media/zr/Data/Models/LLM/Qwen-14B-Chat-Int4",
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},
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NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
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@ -1,15 +1,14 @@
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PROMPT_TEMPLATES = {
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"preprocess_model": {
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"default":
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'根据我们对话的历史，判断本次跟我交流是否需要使用工具，还是可以直接凭借你的知识或者历史记录跟我对话。'
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'以下几种情况要使用工具:\n'
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'1. 实时性的问题，例如查询天气，日期，地点等信息\n'
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'2. 需要数学计算的问题\n'
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'3. 需要查询数据，地点等精确数据\n'
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'4. 需要行业知识的问题\n'
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'5. 需要联网的内容\n'
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'你只要回答一个数字:1代表需要使用工具，你无法为我直接提供服务。0代表不需要使用工具。你应该尽量使用工具\n'
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'你只能回答0或者1'
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'你只要回复0 和 1 ，代表不需要使用工具。以下几种问题不需要使用工具:'
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'1. 介绍一下你自己, 回复0\n'
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'2. 讲一个故事, 回复0\n'
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'3. 给我开一个玩笑, 回复0\n'
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'4. 我当前运行的文件夹是, 回复1\n'
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'5. where is this cat, 回复1\n'
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'6. 介绍一下像极了我大学, 回复1\n'
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'这是我的问题:'
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},
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"llm_model": {
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"default":
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@ -83,7 +82,6 @@ PROMPT_TEMPLATES = {
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'}}}}\n'
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'Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary.\n'
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'Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.\n'
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'history: {history}\n\n'
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'Question: {input}\n\n'
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'Thought: {agent_scratchpad}\n',
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"qwen":
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@ -102,8 +100,96 @@ PROMPT_TEMPLATES = {
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'Begin!\n\n'
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'Question: {input}\n\n'
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'{agent_scratchpad}\n\n',
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},
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"postprocess_model": {
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"default": "{{input}}",
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}
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}
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TOOL_CONFIG = {
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"search_local_knowledgebase": {
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"use": False,
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"top_k": 3,
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"score_threshold": 1,
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"conclude_prompt": {
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"with_result":
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'<指令>根据已知信息，简洁和专业的来回答问题。如果无法从中得到答案，请说 "根据已知信息无法回答该问题"，'
|
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'不允许在答案中添加编造成分，答案请使用中文。 </指令>\n'
|
||||
'<已知信息>{{ context }}</已知信息>\n'
|
||||
'<问题>{{ question }}</问题>\n',
|
||||
"without_result":
|
||||
'请你根据我的提问回答我的问题:\n'
|
||||
'{{ question }}\n'
|
||||
'请注意，你必须在回答结束后强调，你的回答是根据你的经验回答而不是参考资料回答的。\n',
|
||||
}
|
||||
},
|
||||
"search_internet": {
|
||||
"use": False,
|
||||
"search_engine_name": "bing",
|
||||
"search_engine_config":
|
||||
{
|
||||
"bing": {
|
||||
"result_len": 3,
|
||||
"bing_search_url": "https://api.bing.microsoft.com/v7.0/search",
|
||||
"bing_key": "your bing key",
|
||||
},
|
||||
"metaphor": {
|
||||
"result_len": 3,
|
||||
"metaphor_api_key": "",
|
||||
"split_result": False,
|
||||
"chunk_size": 500,
|
||||
"chunk_overlap": 0,
|
||||
},
|
||||
"duckduckgo": {
|
||||
"result_len": 3
|
||||
}
|
||||
},
|
||||
"top_k": 10,
|
||||
"verbose": "Origin",
|
||||
"conclude_prompt":
|
||||
"<指令>这是搜索到的互联网信息，请你根据这些信息进行提取并有调理，简洁的回答问题。如果无法从中得到答案，请说 “无法搜索到能回答问题的内容”。 "
|
||||
"</指令>\n<已知信息>{{ context }}</已知信息>\n"
|
||||
"<问题>\n"
|
||||
"{{ question }}\n"
|
||||
"</问题>\n"
|
||||
},
|
||||
"arxiv": {
|
||||
"use": False,
|
||||
},
|
||||
"shell": {
|
||||
"use": False,
|
||||
},
|
||||
"weather_check": {
|
||||
"use": False,
|
||||
"api-key": "your key",
|
||||
},
|
||||
"search_youtube": {
|
||||
"use": False,
|
||||
},
|
||||
"wolfram": {
|
||||
"use": False,
|
||||
},
|
||||
"calculate": {
|
||||
"use": False,
|
||||
},
|
||||
|
||||
# Use THUDM/cogvlm-chat-hf as default
|
||||
|
||||
"vqa_processor": {
|
||||
"use": False,
|
||||
"model_path": "your model path",
|
||||
"tokenizer_path": "your tokenizer path",
|
||||
"device": "cuda:1"
|
||||
},
|
||||
|
||||
# Use Qwen/Qwen-Audio-Chat as default
|
||||
|
||||
"aqa_processor": {
|
||||
"use": False,
|
||||
"model_path": "your model path",
|
||||
"tokenizer_path": "yout tokenizer path",
|
||||
"device": "cuda:2"
|
||||
},
|
||||
|
||||
}
|
||||
|
||||
@ -40,6 +40,8 @@ FSCHAT_MODEL_WORKERS = {
|
||||
"device": LLM_DEVICE,
|
||||
# False,'vllm',使用的推理加速框架,使用vllm如果出现HuggingFace通信问题，参见doc/FAQ
|
||||
# vllm对一些模型支持还不成熟，暂时默认关闭
|
||||
# fschat=0.2.33的代码有bug, 如需使用，源码修改fastchat.server.vllm_worker，
|
||||
# 将103行中sampling_params = SamplingParams的参数stop=list(stop)修改为stop= [i for i in stop if i!=""]
|
||||
"infer_turbo": False,
|
||||
|
||||
# model_worker多卡加载需要配置的参数
|
||||
@ -90,12 +92,14 @@ FSCHAT_MODEL_WORKERS = {
|
||||
# 'disable_log_requests': False
|
||||
|
||||
},
|
||||
"chatglm3-6b": {
|
||||
"device": "cuda",
|
||||
},
|
||||
"Qwen1.5-0.5B-Chat": {
|
||||
# 可以如下示例方式更改默认配置
|
||||
# "Qwen-1_8B-Chat": { # 使用default中的IP和端口
|
||||
# "device": "cpu",
|
||||
# },
|
||||
"chatglm3-6b": { # 使用default中的IP和端口
|
||||
"device": "cuda",
|
||||
},
|
||||
|
||||
# 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,
|
||||
@ -124,11 +128,14 @@ FSCHAT_MODEL_WORKERS = {
|
||||
"tiangong-api": {
|
||||
"port": 21009,
|
||||
},
|
||||
"gemini-api": {
|
||||
"port": 21010,
|
||||
},
|
||||
}
|
||||
|
||||
# fastchat multi model worker server
|
||||
FSCHAT_MULTI_MODEL_WORKERS = {
|
||||
# TODO:
|
||||
}
|
||||
|
||||
# fastchat controller server
|
||||
FSCHAT_CONTROLLER = {
|
||||
"host": DEFAULT_BIND_HOST,
|
||||
"port": 20001,
|
||||
|
||||
@ -227,5 +227,6 @@ def initialize_glm3_agent(
|
||||
tools=tools,
|
||||
memory=memory,
|
||||
tags=tags_,
|
||||
intermediate_steps=[],
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
@ -20,7 +20,6 @@ from langchain.tools.base import BaseTool
|
||||
from langchain.tools.render import format_tool_to_openai_function
|
||||
from server.utils import get_prompt_template
|
||||
|
||||
|
||||
HUMAN_MESSAGE_TEMPLATE = "{input}\n\n{agent_scratchpad}"
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -108,16 +107,16 @@ class QwenChatAgent(LLMSingleActionAgent):
|
||||
|
||||
@classmethod
|
||||
def from_llm_and_tools(
|
||||
cls,
|
||||
llm: BaseLanguageModel,
|
||||
tools: Sequence[BaseTool],
|
||||
prompt: str = None,
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
output_parser: Optional[AgentOutputParser] = None,
|
||||
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
|
||||
input_variables: Optional[List[str]] = None,
|
||||
memory_prompts: Optional[List[BaseChatPromptTemplate]] = None,
|
||||
**kwargs: Any,
|
||||
cls,
|
||||
llm: BaseLanguageModel,
|
||||
tools: Sequence[BaseTool],
|
||||
prompt: str = None,
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
output_parser: Optional[AgentOutputParser] = None,
|
||||
human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
|
||||
input_variables: Optional[List[str]] = None,
|
||||
memory_prompts: Optional[List[BaseChatPromptTemplate]] = None,
|
||||
**kwargs: Any,
|
||||
) -> QwenChatAgent:
|
||||
"""Construct an agent from an LLM and tools."""
|
||||
cls._validate_tools(tools)
|
||||
@ -148,28 +147,29 @@ class QwenChatAgent(LLMSingleActionAgent):
|
||||
|
||||
|
||||
def initialize_qwen_agent(
|
||||
tools: Sequence[BaseTool],
|
||||
llm: BaseLanguageModel,
|
||||
prompt: str = None,
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
memory: Optional[ConversationBufferWindowMemory] = None,
|
||||
agent_kwargs: Optional[dict] = None,
|
||||
*,
|
||||
return_direct: Optional[bool] = None,
|
||||
tags: Optional[Sequence[str]] = None,
|
||||
**kwargs: Any,
|
||||
tools: Sequence[BaseTool],
|
||||
llm: BaseLanguageModel,
|
||||
prompt: str = None,
|
||||
callback_manager: Optional[BaseCallbackManager] = None,
|
||||
memory: Optional[ConversationBufferWindowMemory] = None,
|
||||
agent_kwargs: Optional[dict] = None,
|
||||
*,
|
||||
return_direct: Optional[bool] = None,
|
||||
tags: Optional[Sequence[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AgentExecutor:
|
||||
tags_ = list(tags) if tags else []
|
||||
agent_kwargs = agent_kwargs or {}
|
||||
|
||||
if isinstance(return_direct, bool): # can make all tools return directly
|
||||
if isinstance(return_direct, bool): # can make all tools return directly
|
||||
tools = [t.copy(update={"return_direct": return_direct}) for t in tools]
|
||||
|
||||
agent_obj = QwenChatAgent.from_llm_and_tools(
|
||||
llm=llm,
|
||||
tools=tools,
|
||||
prompt=prompt,
|
||||
callback_manager=callback_manager, **agent_kwargs
|
||||
callback_manager=callback_manager,
|
||||
**agent_kwargs
|
||||
)
|
||||
return AgentExecutor.from_agent_and_tools(
|
||||
agent=agent_obj,
|
||||
|
||||
@ -82,7 +82,6 @@ def create_models_chains(history, history_len, prompts, models, tools, callbacks
|
||||
llm=models["action_model"],
|
||||
tools=tools,
|
||||
prompt=prompts["action_model"],
|
||||
input_variables=["input", "intermediate_steps", "history"],
|
||||
memory=memory,
|
||||
# callback_manager=BaseCallbackManager(handlers=callbacks),
|
||||
verbose=True,
|
||||
|
||||
@ -10,7 +10,7 @@ from datetime import datetime
|
||||
import os
|
||||
import re
|
||||
import time
|
||||
from configs import (LLM_MODEL_CONFIG, SUPPORT_AGENT_MODELS)
|
||||
from configs import (LLM_MODEL_CONFIG, SUPPORT_AGENT_MODELS, TOOL_CONFIG)
|
||||
import uuid
|
||||
from typing import List, Dict
|
||||
|
||||
@ -204,12 +204,12 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
|
||||
import importlib
|
||||
importlib.reload(model_config_py)
|
||||
|
||||
tools = list(model_config_py.TOOL_CONFIG.keys())
|
||||
tools = list(TOOL_CONFIG.keys())
|
||||
with st.expander("工具栏"):
|
||||
for tool in tools:
|
||||
is_selected = st.checkbox(tool, value=model_config_py.TOOL_CONFIG[tool]["use"], key=tool)
|
||||
is_selected = st.checkbox(tool, value=TOOL_CONFIG[tool]["use"], key=tool)
|
||||
if is_selected:
|
||||
selected_tool_configs[tool] = model_config_py.TOOL_CONFIG[tool]
|
||||
selected_tool_configs[tool] = TOOL_CONFIG[tool]
|
||||
|
||||
if llm_model is not None:
|
||||
model_config['llm_model'][llm_model] = LLM_MODEL_CONFIG['llm_model'][llm_model]
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user