chatchat-space · liunux4odoo · Sep 28, 2023 · Sep 15, 2023 · Sep 15, 2023 · Sep 15, 2023
diff --git a/README.md b/README.md
@@ -57,6 +57,25 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 
 ---
 
+## 环境最低要求
+
+想顺利运行本代码，请按照以下的最低要求进行配置：
++ Python版本: >= 3.8.5, < 3.11
++ Cuda版本: >= 11.7, 且能顺利安装Python
+
+如果想要顺利在GPU运行本地模型(int4版本)，你至少需要以下的硬件配置:
+
++ chatglm2-6b & LLaMA-7B  最低显存要求: 7GB   推荐显卡: RTX 3060, RTX 2060
++ LLaMA-13B 最低显存要求: 11GB  推荐显卡: RTX 2060 12GB, RTX3060 12GB, RTX3080, RTXA2000 
++ Qwen-14B-Chat 最低显存要求: 13GB 推荐显卡: RTX 3090
++ LLaMA-30B 最低显存要求: 22GB  推荐显卡：RTX A5000,RTX 3090,RTX 4090,RTX 6000,Tesla V100,RTX Tesla P40 
++ LLaMA-65B 最低显存要求: 40GB  推荐显卡：A100,A40,A6000
+
+如果是int8 则显存x1.5 fp16 x2.5的要求
+如：使用fp16 推理Qwen-7B-Chat 模型 则需要使用16GB显存。
+
+以上仅为估算，实际情况以nvidia-smi占用为准。
+
 ## 变更日志
 
 参见 [版本更新日志](https://github.com/imClumsyPanda/langchain-ChatGLM/releases)。
@@ -112,27 +131,29 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 - [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0)
 - [baichuan-inc/baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B)
 - [internlm/internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)
-- [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat)
+- [Qwen/Qwen-7B-Chat/Qwen-14B-Chat](https://huggingface.co/Qwen/)
 - [HuggingFaceH4/starchat-beta](https://huggingface.co/HuggingFaceH4/starchat-beta)
 - [FlagAlpha/Llama2-Chinese-13b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat) and others
 - [BAAI/AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B)
 - [all models of OpenOrca](https://huggingface.co/Open-Orca)
 - [Spicyboros](https://huggingface.co/jondurbin/spicyboros-7b-2.2?not-for-all-audiences=true) + [airoboros 2.2](https://huggingface.co/jondurbin/airoboros-l2-13b-2.2)
 - [VMware&#39;s OpenLLaMa OpenInstruct](https://huggingface.co/VMware/open-llama-7b-open-instruct)
+- [baichuan2-7b/baichuan2-13b](https://huggingface.co/baichuan-inc)
 - 任何 [EleutherAI](https://huggingface.co/EleutherAI) 的 pythia 模型，如 [pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b)
 - 在以上模型基础上训练的任何 [Peft](https://github.com/huggingface/peft) 适配器。为了激活，模型路径中必须有 `peft` 。注意：如果加载多个peft模型，你可以通过在任何模型工作器中设置环境变量 `PEFT_SHARE_BASE_WEIGHTS=true` 来使它们共享基础模型的权重。
 
 以上模型支持列表可能随 [FastChat](https://github.com/lm-sys/FastChat) 更新而持续更新，可参考 [FastChat 已支持模型列表](https://github.com/lm-sys/FastChat/blob/main/docs/model_support.md)。
 
-
 除本地模型外，本项目也支持直接接入 OpenAI API、智谱AI等在线模型，具体设置可参考 `configs/model_configs.py.example` 中的 `llm_model_dict` 的配置信息。
 
-在线 LLM 模型目前已支持： 
+在线 LLM 模型目前已支持：
+
 - [ChatGPT](https://api.openai.com)
 - [智谱AI](http://open.bigmodel.cn)
 - [MiniMax](https://api.minimax.chat)
 - [讯飞星火](https://xinghuo.xfyun.cn)
 - [百度千帆](https://cloud.baidu.com/product/wenxinworkshop?track=dingbutonglan)
+- [阿里云通义千问](https://dashscope.aliyun.com/)
 
 项目中默认使用的 LLM 类型为 `THUDM/chatglm2-6b`，如需使用其他 LLM 类型，请在 [configs/model_config.py] 中对 `llm_model_dict` 和 `LLM_MODEL` 进行修改。
 
@@ -157,9 +178,11 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 - [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese)
 - [nghuyong/ernie-3.0-nano-zh](https://huggingface.co/nghuyong/ernie-3.0-nano-zh)
 - [nghuyong/ernie-3.0-base-zh](https://huggingface.co/nghuyong/ernie-3.0-base-zh)
+- [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-base-zh)
+- [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-large-zh)
 - [OpenAI/text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings)
 
-项目中默认使用的 Embedding 类型为 `moka-ai/m3e-base`，如需使用其他 Embedding 类型，请在 [configs/model_config.py] 中对 `embedding_model_dict` 和 `EMBEDDING_MODEL` 进行修改。
+项目中默认使用的 Embedding 类型为 `sensenova/piccolo-base-zh`，如需使用其他 Embedding 类型，请在 [configs/model_config.py] 中对 `embedding_model_dict` 和 `EMBEDDING_MODEL` 进行修改。
 
 ---
 
@@ -187,15 +210,27 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 
 关于如何使用自定义分词器和贡献自己的分词器，可以参考[Text Splitter 贡献说明](docs/splitter.md)。
 
+## Agent生态
+### 基础的Agent
+在本版本中，我们实现了一个简单的基于OpenAI的React的Agent模型，目前，经过我们测试，仅有以下两个模型支持：
++ OpenAI GPT4
++ ChatGLM2-130B
+
+目前版本的Agent仍然需要对提示词进行大量调试，调试位置
+
+### 构建自己的Agent工具
+
+详见 [自定义Agent说明](docs/自定义Agent.md)
+
 ## Docker 部署
 
-🐳 Docker 镜像地址: `registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.3)`
+🐳 Docker 镜像地址: `registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.5)`
 
 ```shell
-docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.3
+docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.5
 ```
 
-- 该版本镜像大小 `35.3GB`，使用 `v0.2.3`，以 `nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04` 为基础镜像
+- 该版本镜像大小 `35.3GB`，使用 `v0.2.5`，以 `nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04` 为基础镜像
 - 该版本内置两个 `embedding` 模型：`m3e-large`，`text2vec-bge-large-chinese`，默认启用后者，内置 `chatglm2-6b-32k`
 - 该版本目标为方便一键部署使用，请确保您已经在Linux发行版上安装了NVIDIA驱动程序
 - 请注意，您不需要在主机系统上安装CUDA工具包，但需要安装 `NVIDIA Driver` 以及 `NVIDIA Container Toolkit`，请参考[安装指南](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
@@ -391,22 +426,26 @@ CUDA_VISIBLE_DEVICES=0,1 python startup.py -a
       - [X] .csv
       - [ ] .xlsx
     - [ ] 分词及召回
-      - [ ] 接入不同类型 TextSplitter
-      - [ ] 优化依据中文标点符号设计的 ChineseTextSplitter
+      - [X] 接入不同类型 TextSplitter
+      - [X] 优化依据中文标点符号设计的 ChineseTextSplitter
       - [ ] 重新实现上下文拼接召回
     - [ ] 本地网页接入
     - [ ] SQL 接入
     - [ ] 知识图谱/图数据库接入
   - [X] 搜索引擎接入
     - [X] Bing 搜索
     - [X] DuckDuckGo 搜索
-  - [ ] Agent 实现
+  - [X] Agent 实现
+    - [X] 基础React形式的Agent实现，包括调用计算器等
+    - [X] Langchain 自带的Agent实现和调用
+    - [ ] 更多模型的Agent支持
+    - [ ] 更多工具
 - [X] LLM 模型接入
   - [X] 支持通过调用 [FastChat](https://github.com/lm-sys/fastchat) api 调用 llm
-  - [ ] 支持 ChatGLM API 等 LLM API 的接入
+  - [X] 支持 ChatGLM API 等 LLM API 的接入
 - [X] Embedding 模型接入
   - [X] 支持调用 HuggingFace 中各开源 Emebdding 模型
-  - [ ] 支持 OpenAI Embedding API 等 Embedding API 的接入
+  - [X] 支持 OpenAI Embedding API 等 Embedding API 的接入
 - [X] 基于 FastAPI 的 API 方式调用
 - [X] Web UI
   - [X] 基于 Streamlit 的 Web UI
@@ -417,4 +456,12 @@ CUDA_VISIBLE_DEVICES=0,1 python startup.py -a
 
 <img src="img/qr_code_64.jpg" alt="二维码" width="300" height="300" />
 
-🎉 langchain-ChatGLM 项目微信交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。
+🎉 langchain-Chatchat 项目微信交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。
+
+
+## 关注我们
+
+<img src="img/official_account.png" alt="图片" width="900" height="300" />
+🎉 langchain-Chatchat 项目官方公众号，欢迎扫码关注。
+
+
diff --git a/README_en.md b/README_en.md
@@ -56,6 +56,25 @@ docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/ch
 
 ---
 
+## Environment Minimum Requirements
+
+To run this code smoothly, please configure it according to the following minimum requirements:
++ Python version: >= 3.8.5, < 3.11
++ Cuda version: >= 11.7, with Python installed.
+
+If you want to run the native model (int4 version) on the GPU without problems, you need at least the following hardware configuration.
+
++ chatglm2-6b & LLaMA-7B Minimum RAM requirement: 7GB Recommended graphics cards: RTX 3060, RTX 2060
++ LLaMA-13B Minimum graphics memory requirement: 11GB Recommended cards: RTX 2060 12GB, RTX3060 12GB, RTX3080, RTXA2000 
++ Qwen-14B-Chat Minimum memory requirement: 13GB Recommended graphics card: RTX 3090
++ LLaMA-30B Minimum Memory Requirement: 22GB Recommended Cards: RTX A5000,RTX 3090,RTX 4090,RTX 6000,Tesla V100,RTX Tesla P40 
++ Minimum memory requirement for LLaMA-65B: 40GB Recommended cards: A100,A40,A6000
+
+If int8 then memory x1.5 fp16 x2.5 requirement.
+For example: using fp16 to reason about the Qwen-7B-Chat model requires 16GB of video memory.
+
+The above is only an estimate, the actual situation is based on nvidia-smi occupancy.
+
 ## Change Log
 
 plese refer to [version change log](https://github.com/imClumsyPanda/langchain-ChatGLM/releases)
@@ -105,18 +124,31 @@ The project use [FastChat](https://github.com/lm-sys/FastChat) to provide the AP
 - [WizardLM/WizardCoder-15B-V1.0](https://huggingface.co/WizardLM/WizardCoder-15B-V1.0)
 - [baichuan-inc/baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B)
 - [internlm/internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b)
-- [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat)
+- [Qwen/Qwen-7B-Chat/Qwen-14B-Chat](https://huggingface.co/Qwen/)
 - [HuggingFaceH4/starchat-beta](https://huggingface.co/HuggingFaceH4/starchat-beta)
 - [FlagAlpha/Llama2-Chinese-13b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat) and other models of FlagAlpha
 - [BAAI/AquilaChat-7B](https://huggingface.co/BAAI/AquilaChat-7B)
 - [all models of OpenOrca](https://huggingface.co/Open-Orca)
 - [Spicyboros](https://huggingface.co/jondurbin/spicyboros-7b-2.2?not-for-all-audiences=true) + [airoboros 2.2](https://huggingface.co/jondurbin/airoboros-l2-13b-2.2)
+- [baichuan2-7b/baichuan2-13b](https://huggingface.co/baichuan-inc)
 - [VMware&#39;s OpenLLaMa OpenInstruct](https://huggingface.co/VMware/open-llama-7b-open-instruct)
 
 * Any [EleutherAI](https://huggingface.co/EleutherAI) pythia model such as [pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b)(任何 [EleutherAI](https://huggingface.co/EleutherAI) 的 pythia 模型，如 [pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b))
 * Any [Peft](https://github.com/huggingface/peft) adapter trained on top of a model above. To activate, must have `peft` in the model path. Note: If loading multiple peft models, you can have them share the base model weights by setting the environment variable `PEFT_SHARE_BASE_WEIGHTS=true` in any model worker.
 
-Please refer to `llm_model_dict` in `configs.model_configs.py.example` to invoke OpenAI API.
+
+The above model support list may be updated continuously as [FastChat](https://github.com/lm-sys/FastChat) is updated, see [FastChat Supported Models List](https://github.com/lm-sys/FastChat/blob/main /docs/model_support.md).
+In addition to local models, this project also supports direct access to online models such as OpenAI API, Wisdom Spectrum AI, etc. For specific settings, please refer to the configuration information of `llm_model_dict` in `configs/model_configs.py.example`.
+Online LLM models are currently supported:
+
+- [ChatGPT](https://api.openai.com)
+- [Smart Spectrum AI](http://open.bigmodel.cn)
+- [MiniMax](https://api.minimax.chat)
+- [Xunfei Starfire](https://xinghuo.xfyun.cn)
+- [Baidu Qianfan](https://cloud.baidu.com/product/wenxinworkshop?track=dingbutonglan)
+- [Aliyun Tongyi Qianqian](https://dashscope.aliyun.com/)
+
+The default LLM type used in the project is `THUDM/chatglm2-6b`, if you need to use other LLM types, please modify `llm_model_dict` and `LLM_MODEL` in [configs/model_config.py].
 
 ### Supported Embedding models
 
@@ -129,6 +161,8 @@ Following models are tested by developers with Embedding class of [HuggingFace](
 - [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh)
 - [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh)
 - [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct)
+- [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-base-zh)
+- [sensenova/piccolo-large-zh](https://huggingface.co/sensenova/piccolo-large-zh)
 - [shibing624/text2vec-base-chinese-sentence](https://huggingface.co/shibing624/text2vec-base-chinese-sentence)
 - [shibing624/text2vec-base-chinese-paraphrase](https://huggingface.co/shibing624/text2vec-base-chinese-paraphrase)
 - [shibing624/text2vec-base-multilingual](https://huggingface.co/shibing624/text2vec-base-multilingual)
@@ -137,16 +171,24 @@ Following models are tested by developers with Embedding class of [HuggingFace](
 - [GanymedeNil/text2vec-large-chinese](https://huggingface.co/GanymedeNil/text2vec-large-chinese)
 - [nghuyong/ernie-3.0-nano-zh](https://huggingface.co/nghuyong/ernie-3.0-nano-zh)
 - [nghuyong/ernie-3.0-base-zh](https://huggingface.co/nghuyong/ernie-3.0-base-zh)
+- [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-base-zh)
+- [sensenova/piccolo-base-zh](https://huggingface.co/sensenova/piccolo-large-zh)
 - [OpenAI/text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings)
 
+The default Embedding type used in the project is `sensenova/piccolo-base-zh`, if you want to use other Embedding types, please modify `embedding_model_dict` and `embedding_model_dict` and `embedding_model_dict` in [configs/model_config.py]. MODEL` in [configs/model_config.py].
+
+### Build your own Agent tool!
+
+See [Custom Agent Instructions](docs/自定义Agent.md) for details.
+
 ---
 
 ## Docker Deployment
 
-🐳 Docker image path: `registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.0)`
+🐳 Docker image path: `registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.5)`
 
 ```shell
-docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.0
+docker run -d --gpus all -p 80:8501 registry.cn-beijing.aliyuncs.com/chatchat/chatchat:0.2.5
 ```
 
 - The image size of this version is `33.9GB`, using `v0.2.0`, with `nvidia/cuda:12.1.1-cudnn8-devel-ubuntu22.04` as the base image
@@ -328,17 +370,21 @@ Please refer to [FAQ](docs/FAQ.md)
     - [ ] Structured documents
       - [X] .csv
       - [ ] .xlsx
-    - [ ] TextSplitter and Retriever
-      - [x] multiple TextSplitter
-      - [x] ChineseTextSplitter
+    - [] TextSplitter and Retriever
+      - [X] multiple TextSplitter
+      - [X] ChineseTextSplitter
       - [ ] Reconstructed Context Retriever
     - [ ] Webpage
     - [ ] SQL
     - [ ] Knowledge Database
   - [X] Search Engines
     - [X] Bing
     - [X] DuckDuckGo
-  - [ ] Agent
+  - [X] Agent
+    - [X] Agent implementation in the form of basic React, including calls to calculators, etc.
+    - [X] Langchain's own Agent implementation and calls
+    - [ ] More Agent support for models
+    - [ ] More tools
 - [X] LLM  Models
   - [X] [FastChat](https://github.com/lm-sys/fastchat) -based LLM Models
   - [ ] Mutiply Remote LLM API
@@ -348,3 +394,16 @@ Please refer to [FAQ](docs/FAQ.md)
 - [X] FastAPI-based API
 - [X] Web UI
   - [X] Streamlit -based Web UI
+
+---
+
+## Wechat Group
+
+<img src="img/qr_code_64.jpg" alt="QR Code" width="300" height="300" />
+
+🎉 langchain-Chatchat project WeChat exchange group, if you are also interested in this project, welcome to join the group chat to participate in the discussion and exchange.
+
+## Follow us
+
+<img src="img/official_account.png" alt="image" width="900" height="300" />
+🎉 langchain-Chatchat project official public number, welcome to scan the code to follow.
diff --git a/chains/llmchain_with_history.py b/chains/llmchain_with_history.py
@@ -1,19 +1,12 @@
-from langchain.chat_models import ChatOpenAI
-from configs.model_config import llm_model_dict, LLM_MODEL
-from langchain import LLMChain
+from server.utils import get_ChatOpenAI
+from configs.model_config import LLM_MODEL, TEMPERATURE
+from langchain.chains import LLMChain
 from langchain.prompts.chat import (
     ChatPromptTemplate,
     HumanMessagePromptTemplate,
 )
 
-model = ChatOpenAI(
-    streaming=True,
-    verbose=True,
-    # callbacks=[callback],
-    openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
-    openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
-    model_name=LLM_MODEL
-)
+model = get_ChatOpenAI(model_name=LLM_MODEL, temperature=TEMPERATURE)
 
 
 human_prompt = "{input}"

diff --git a/configs/__init__.py b/configs/__init__.py
@@ -1,4 +1,8 @@
+from .basic_config import *
 from .model_config import *
+from .kb_config import *
 from .server_config import *
+from .prompt_config import *
 
-VERSION = "v0.2.4"
+
+VERSION = "v0.2.5"
diff --git a/configs/basic_config.py.example b/configs/basic_config.py.example
@@ -0,0 +1,22 @@
+import logging
+import os
+import langchain
+
+# 是否显示详细日志
+log_verbose = False
+langchain.verbose = False
+
+
+# 通常情况下不需要更改以下内容
+
+# 日志格式
+LOG_FORMAT = "%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s"
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+logging.basicConfig(format=LOG_FORMAT)
+
+
+# 日志存储路径
+LOG_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "logs")
+if not os.path.exists(LOG_PATH):
+    os.mkdir(LOG_PATH)