enhance

configs/model_config.py

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+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。
+# 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 = ""
+
+# 选用的 Embedding 名称
+EMBEDDING_MODEL = "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"
+
+# 选用的reranker模型
+RERANKER_MODEL = "bge-reranker-large"
+# 是否启用reranker模型
+USE_RERANKER = False
+RERANKER_MAX_LENGTH = 1024
+
+# 是否启用精排
+USE_RANKING = False
+
+# 如果需要在 EMBEDDING_MODEL 中增加自定义的关键字时配置
+EMBEDDING_KEYWORD_FILE = "keywords.txt"
+EMBEDDING_MODEL_OUTPUT_PATH = "output"
+
+# 要运行的 LLM 名称，可以包括本地模型和在线模型。列表中本地模型将在启动项目时全部加载。
+# 列表中第一个模型将作为 API 和 WEBUI 的默认模型。
+# 在这里，我们使用目前主流的两个离线模型，其中，chatglm3-6b 为默认加载模型。
+# 如果你的显存不足，可使用 Qwen-1_8B-Chat, 该模型 FP16 仅需 3.8G显存。
+
+# chatglm3-6b输出角色标签<|user|>及自问自答的问题详见项目wiki->常见问题->Q20.
+
+LLM_MODELS = ["chatglm3-6b", "zhipu-api", "openai-api"]  # "Qwen-1_8B-Chat",
+
+# 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。
+LLM_DEVICE = "auto"
+
+# 历史对话轮数
+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
+TEMPERATURE = 0.7
+# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
+
+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": "",
+    },
+
+    # 具体注册及api key获取请前往 http://open.bigmodel.cn
+    "zhipu-api": {
+        "api_key": "",
+        "version": "chatglm_turbo",  # 可选包括 "chatglm_turbo"
+        "provider": "ChatGLMWorker",
+    },
+
+
+    # 具体注册及api key获取请前往 https://api.minimax.chat/
+    "minimax-api": {
+        "group_id": "",
+        "api_key": "",
+        "is_pro": False,
+        "provider": "MiniMaxWorker",
+    },
+
+
+    # 具体注册及api key获取请前往 https://xinghuo.xfyun.cn/
+    "xinghuo-api": {
+        "APPID": "",
+        "APISecret": "",
+        "api_key": "",
+        "version": "v1.5",  # 你使用的讯飞星火大模型版本，可选包括 "v3.0", "v1.5", "v2.0"
+        "provider": "XingHuoWorker",
+    },
+
+    # 百度千帆 API，申请方式请参考 https://cloud.baidu.com/doc/WENXINWORKSHOP/s/4lilb2lpf
+    "qianfan-api": {
+        "version": "ERNIE-Bot",  # 注意大小写。当前支持 "ERNIE-Bot" 或 "ERNIE-Bot-turbo"， 更多的见官方文档。
+        "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
+        "api_key": "",
+        "secret_key": "",
+        "provider": "QianFanWorker",
+    },
+
+    # 火山方舟 API，文档参考 https://www.volcengine.com/docs/82379
+    "fangzhou-api": {
+        "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。
+        "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
+        "api_key": "",
+        "secret_key": "",
+        "provider": "FangZhouWorker",
+    },
+
+    # 阿里云通义千问 API，文档参考 https://help.aliyun.com/zh/dashscope/developer-reference/api-details
+    "qwen-api": {
+        "version": "qwen-turbo",  # 可选包括 "qwen-turbo", "qwen-plus"
+        "api_key": "",  # 请在阿里云控制台模型服务灵积API-KEY管理页面创建
+        "provider": "QwenWorker",
+        "embed_model": "text-embedding-v1" # embedding 模型名称
+    },
+
+    # 百川 API，申请方式请参考 https://www.baichuan-ai.com/home#api-enter
+    "baichuan-api": {
+        "version": "Baichuan2-53B",  # 当前支持 "Baichuan2-53B"， 见官方文档。
+        "api_key": "",
+        "secret_key": "",
+        "provider": "BaiChuanWorker",
+    },
+
+    # Azure API
+    "azure-api": {
+        "deployment_name": "",  # 部署容器的名字
+        "resource_name": "",  # https://{resource_name}.openai.azure.com/openai/ 填写resource_name的部分，其他部分不要填写
+        "api_version": "",  # API的版本，不是模型版本
+        "api_key": "",
+        "provider": "AzureWorker",
+    },
+
+    # 昆仑万维天工 API https://model-platform.tiangong.cn/
+    "tiangong-api": {
+        "version": "SkyChat-MegaVerse",
+        "api_key": "",
+        "secret_key": "",
+        "provider": "TianGongWorker",
+    },
+
+}
+
+# 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",
+        "nlp_gte_sentence-embedding_chinese-large": "damo/nlp_gte_sentence-embedding_chinese-large",
+        "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",
+        "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",
+        "chatglm3-6b-base": "THUDM/chatglm3-6b-base",
+
+        "Qwen-1_8B": "Qwen/Qwen-1_8B",
+        "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat",
+        "Qwen-1_8B-Chat-Int8": "Qwen/Qwen-1_8B-Chat-Int8",
+        "Qwen-1_8B-Chat-Int4": "Qwen/Qwen-1_8B-Chat-Int4",
+
+        "Qwen-7B": "Qwen/Qwen-7B",
+        "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
+
+        "Qwen-14B": "Qwen/Qwen-14B",
+        "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
+
+        "Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8",
+        # 在新版的transformers下需要手动修改模型的config.json文件，在quantization_config字典中
+        # 增加`disable_exllama:true` 字段才能启动qwen的量化模型
+        "Qwen-14B-Chat-Int4": "Qwen/Qwen-14B-Chat-Int4",
+
+        "Qwen-72B": "Qwen/Qwen-72B",
+        "Qwen-72B-Chat": "Qwen/Qwen-72B-Chat",
+        "Qwen-72B-Chat-Int8": "Qwen/Qwen-72B-Chat-Int8",
+        "Qwen-72B-Chat-Int4": "Qwen/Qwen-72B-Chat-Int4",
+
+        "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",
+
+        "agentlm-7b": "THUDM/agentlm-7b",
+        "agentlm-13b": "THUDM/agentlm-13b",
+        "agentlm-70b": "THUDM/agentlm-70b",
+
+        "Yi-34B-Chat": "01-ai/Yi-34B-Chat",
+    },
+    "reranker":{
+        "bge-reranker-large":"BAAI/bge-reranker-large",
+        "bge-reranker-base":"BAAI/bge-reranker-base",
+        #TODO 增加在线reranker，如cohere
+    }
+}
+
+
+# 通常情况下不需要更改以下内容
+
+# nltk 模型存储路径
+NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
+
+VLLM_MODEL_DICT = {
+    "aquila-7b": "BAAI/Aquila-7B",
+    "aquilachat-7b": "BAAI/AquilaChat-7B",
+
+    "baichuan-7b": "baichuan-inc/Baichuan-7B",
+    "baichuan-13b": "baichuan-inc/Baichuan-13B",
+    "baichuan-13b-chat": "baichuan-inc/Baichuan-13B-Chat",
+
+    "chatglm2-6b": "THUDM/chatglm2-6b",
+    "chatglm2-6b-32k": "THUDM/chatglm2-6b-32k",
+    "chatglm3-6b": "THUDM/chatglm3-6b",
+    "chatglm3-6b-32k": "THUDM/chatglm3-6b-32k",
+
+    "BlueLM-7B-Chat": "vivo-ai/BlueLM-7B-Chat",
+    "BlueLM-7B-Chat-32k": "vivo-ai/BlueLM-7B-Chat-32k",
+
+    # 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
+    # "bloom": "bigscience/bloom",
+    # "bloomz": "bigscience/bloomz",
+    # "bloomz-560m": "bigscience/bloomz-560m",
+    # "bloomz-7b1": "bigscience/bloomz-7b1",
+    # "bloomz-1b7": "bigscience/bloomz-1b7",
+
+    "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-1_8B": "Qwen/Qwen-1_8B",
+    "Qwen-1_8B-Chat": "Qwen/Qwen-1_8B-Chat",
+    "Qwen-1_8B-Chat-Int8": "Qwen/Qwen-1_8B-Chat-Int8",
+    "Qwen-1_8B-Chat-Int4": "Qwen/Qwen-1_8B-Chat-Int4",
+
+    "Qwen-7B": "Qwen/Qwen-7B",
+    "Qwen-7B-Chat": "Qwen/Qwen-7B-Chat",
+
+    "Qwen-14B": "Qwen/Qwen-14B",
+    "Qwen-14B-Chat": "Qwen/Qwen-14B-Chat",
+    "Qwen-14B-Chat-Int8": "Qwen/Qwen-14B-Chat-Int8",
+    "Qwen-14B-Chat-Int4": "Qwen/Qwen-14B-Chat-Int4",
+
+    "Qwen-72B": "Qwen/Qwen-72B",
+    "Qwen-72B-Chat": "Qwen/Qwen-72B-Chat",
+    "Qwen-72B-Chat-Int8": "Qwen/Qwen-72B-Chat-Int8",
+    "Qwen-72B-Chat-Int4": "Qwen/Qwen-72B-Chat-Int4",
+
+    "agentlm-7b": "THUDM/agentlm-7b",
+    "agentlm-13b": "THUDM/agentlm-13b",
+    "agentlm-70b": "THUDM/agentlm-70b",
+
+}
+
+# 你认为支持Agent能力的模型，可以在这里添加，添加后不会出现可视化界面的警告
+# 经过我们测试，原生支持Agent的模型仅有以下几个
+SUPPORT_AGENT_MODEL = [
+    "azure-api",
+    "openai-api",
+    "qwen-api",
+    "Qwen",
+    "chatglm3",
+    "xinghuo-api",
+]

configs/prompt_config.py

@@ -0,0 +1,126 @@
+# prompt模板使用Jinja2语法，简单点就是用双大括号代替f-string的单大括号
+# 本配置文件支持热加载，修改prompt模板后无需重启服务。
+
+# LLM对话支持的变量：
+#   - input: 用户输入内容
+
+# 知识库和搜索引擎对话支持的变量：
+#   - context: 从检索结果拼接的知识文本
+#   - question: 用户提出的问题
+
+# Agent对话支持的变量：
+
+#   - tools: 可用的工具列表
+#   - tool_names: 可用的工具名称列表
+#   - history: 用户和Agent的对话历史
+#   - input: 用户输入内容
+#   - agent_scratchpad: Agent的思维记录
+
+PROMPT_TEMPLATES = {
+    "llm_chat": {
+        "default":
+            '{{ input }}',
+
+        "with_history":
+            'The following is a friendly conversation between a human and an AI. '
+            'The AI is talkative and provides lots of specific details from its context. '
+            'If the AI does not know the answer to a question, it truthfully says it does not know.\n\n'
+            'Current conversation:\n'
+            '{history}\n'
+            'Human: {input}\n'
+            'AI:',
+
+        "py":
+            '你是一个聪明的代码助手，请你给我写出简单的py代码。 \n'
+            '{{ input }}',
+    },
+
+    "knowledge_base_chat": {
+        "default":
+            '<指令>你是一个电力公司相关的专家，请完全依据已知信息的内容，先找出与问题相关的信息，然后再根据这些相关信息回答简洁、专业地来回答问题。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”，'
+            '不允许在答案中添加编造成分，不回答与问题无关的内容，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',
+
+        "text":
+            '<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”，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',
+
+        "empty":  # 搜不到知识库的时候使用
+            '请你回答我的问题:\n'
+            '{{ question }}\n\n',
+    },
+
+
+    "search_engine_chat": {
+        "default":
+            '<指令>这是我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>{{ question }}</问题>\n',
+
+        "search":
+            '<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”，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',
+    },
+
+
+    "agent_chat": {
+        "default":
+            'Answer the following questions as best you can. If it is in order, you can use some tools appropriately. '
+            'You have access to the following tools:\n\n'
+            '{tools}\n\n'
+            'Use the following format:\n'
+            'Question: the input question you must answer1\n'
+            'Thought: you should always think about what to do and what tools to use.\n'
+            'Action: the action to take, should be one of [{tool_names}]\n'
+            'Action Input: the input to the action\n'
+            'Observation: the result of the action\n'
+            '... (this Thought/Action/Action Input/Observation can be repeated zero or more times)\n'
+            'Thought: I now know the final answer\n'
+            'Final Answer: the final answer to the original input question\n'
+            'Begin!\n\n'
+            'history: {history}\n\n'
+            'Question: {input}\n\n'
+            'Thought: {agent_scratchpad}\n',
+
+        "ChatGLM3":
+            'You can answer using the tools, or answer directly using your knowledge without using the tools. '
+            'Respond to the human as helpfully and accurately as possible.\n'
+            'You have access to the following tools:\n'
+            '{tools}\n'
+            'Use a json blob to specify a tool by providing an action key (tool name) '
+            'and an action_input key (tool input).\n'
+            'Valid "action" values: "Final Answer" or  [{tool_names}]'
+            'Provide only ONE action per $JSON_BLOB, as shown:\n\n'
+            '```\n'
+            '{{{{\n'
+            '  "action": $TOOL_NAME,\n'
+            '  "action_input": $INPUT\n'
+            '}}}}\n'
+            '```\n\n'
+            'Follow this format:\n\n'
+            'Question: input question to answer\n'
+            'Thought: consider previous and subsequent steps\n'
+            'Action:\n'
+            '```\n'
+            '$JSON_BLOB\n'
+            '```\n'
+            'Observation: action result\n'
+            '... (repeat Thought/Action/Observation N times)\n'
+            'Thought: I know what to respond\n'
+            'Action:\n'
+            '```\n'
+            '{{{{\n'
+            '  "action": "Final Answer",\n'
+            '  "action_input": "Final response to human"\n'
+            '}}}}\n'
+            'Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. '
+            'Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.\n'
+            'history: {history}\n\n'
+            'Question: {input}\n\n'
+            'Thought: {agent_scratchpad}',
+    }
+}

document_loaders/mywordload.py

@@ -7,6 +7,8 @@ from docx.oxml.text.paragraph import CT_P
 from docx.oxml.table import CT_Tbl
 from docx.table import _Cell, Table
 from docx.text.paragraph import Paragraph
+#from langchain.document_loaders.unstructured import UnstructuredFileLoader
+#from langchain.document_loaders.word_document import Docx2txtLoader 
 
 class RapidWordLoader(UnstructuredFileLoader):
     def _get_elements(self) -> List:
@@ -57,6 +59,8 @@ class RapidWordLoader(UnstructuredFileLoader):
                 doc = docxDocument(filepath)
                 for block in iter_block_items(doc):
                     if isinstance(block,Paragraph):
+
+                        #print(f"Paragraph:{block.text}")
                         resp += (block.text + "\n\n")
                     elif isinstance(block, Table):
                         resp += read_table(block) + "\n"
@@ -68,10 +72,12 @@ class RapidWordLoader(UnstructuredFileLoader):
         
         text = word2text(self.file_path)
         from unstructured.partition.text import partition_text
-        return partition_text(text=text, **self.unstructured_kwargs)
+        return partition_text(text=text, paragraph_grouper = False, **self.unstructured_kwargs)
-
 
 if __name__ == "__main__":
-    loader = RapidWordLoader(file_path="/Users/wangvivi/Desktop/MySelf/AI/Test/国家电网公司供电企业组织机构规范标准.docx")
+    loader = RapidWordLoader(file_path="/Users/wangvivi/Desktop/Work/思极GPT/数字化部/设备类all/sb389/10kV带电作业用绝缘斗臂车.docx")
+    #loader = Docx2txtLoader(file_path="/Users/wangvivi/Desktop/Work/思极GPT/数字化部/设备类all/sb389/10kV带电作业用绝缘斗臂车.docx")
+    #loader = RapidWordLoader(file_path="/Users/wangvivi/Desktop/MySelf/AI/Test/这是一个测试文档_副本2.docx")
     docs = loader.load()
     print(docs)
+

server/agent/tools/weather_check.py

@@ -27,7 +27,7 @@ from server.agent import model_container
 from pydantic import BaseModel, Field
 
 ## 使用和风天气API查询天气
-KEY = "ac880e5a877042809ac7ffdd19d95b0d"
+KEY = "1234567890wangweiwei"
 # key长这样，这里提供了示例的key，这个key没法使用，你需要自己去注册和风天气的账号，然后在这里填入你的key
 
 

server/agent/tools_select.py

@@ -19,7 +19,7 @@ tools = [
     Tool.from_function(
         func=weathercheck,
         name="weather_check",
-        description="",
+        description="Use this tools to answer questons about weather",
         args_schema=WhetherSchema,
     ),
     Tool.from_function(

server/chat/agent_chat.py

@@ -17,7 +17,7 @@ from server.chat.utils import History
 import json
 from server.agent import model_container
 from server.knowledge_base.kb_service.base import get_kb_details
-
+from fastapi.responses import StreamingResponse
 
 async def agent_chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
                      history: List[History] = Body([],
@@ -29,7 +29,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
                                                    ),
                      stream: bool = Body(False, description="流式输出"),
                      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),
+                     temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=0.9),
                      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",
                                              description="使用的prompt模板名称(在configs/prompt_config.py中配置)"),
@@ -59,6 +59,8 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
         kb_list = {x["kb_name"]: x for x in get_kb_details()}
         model_container.DATABASE = {name: details['kb_info'] for name, details in kb_list.items()}
 
+        print(f"agent_chat_iterator model_container.DATABASE:{model_container.DATABASE}")
+        print(f"agent_chat_iterator temperature:{temperature}")
         if Agent_MODEL:
             ## 如果有指定使用Agent模型来完成任务
             model_agent = get_ChatOpenAI(
@@ -68,8 +70,10 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
                 callbacks=[callback],
             )
             model_container.MODEL = model_agent
+            print(f"111 agent_chat_iterator :model_container.MODEL:{ model_container.MODEL}")
         else:
             model_container.MODEL = model
+            print(f"222 agent_chat_iterator :model_container.MODEL:{model_container.MODEL}")
 
         prompt_template = get_prompt_template("agent_chat", prompt_name)
         prompt_template_agent = CustomPromptTemplate(
@@ -91,6 +95,7 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
                 memory.chat_memory.add_ai_message(message.content)
 
         if "chatglm3" in model_container.MODEL.model_name:
+            print(f"model_container.MODEL.model_name is chatglm3")
             agent_executor = initialize_glm3_agent(
                 llm=model,
                 tools=tools,
@@ -180,8 +185,13 @@ async def agent_chat(query: str = Body(..., description="用户输入", examples
             yield json.dumps({"answer": answer, "final_answer": final_answer}, ensure_ascii=False)
         await task
 
-    return EventSourceResponse(agent_chat_iterator(query=query,
+    return StreamingResponse(agent_chat_iterator(query=query,
                                                  history=history,
                                                  model_name=model_name,
                                                  prompt_name=prompt_name),
-                             )
+                             media_type="text/event-stream")
+    # return EventSourceResponse(agent_chat_iterator(query=query,
+    #                                              history=history,
+    #                                              model_name=model_name,
+    #                                              prompt_name=prompt_name),
+    #                          )

webui_pages/dialogue/dialogue.py

@@ -350,7 +350,9 @@ def dialogue_page(api: ApiRequest, is_lite: bool = False):
                                         temperature=temperature,
                                         ):
                     try:
+                        print(f"1111自定义Agent问答, d:{d}")
                         d = json.loads(d)
+                        print(f"22222自定义Agent问答, d:{d}")
                     except:
                         pass
                     if error_msg := check_error_msg(d):  # check whether error occured