Merge pull request #72 from WNJXYK/main

[ENH] Add knowledge base for LawGPT

LICENSE

@@ -1,674 +0,0 @@
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-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -8,8 +8,7 @@
 
 <p align="center">
     <a href="https://github.com/pengxiao-song/LaWGPT/wiki"><img src="https://img.shields.io/badge/docs-Wiki-brightgreen"></a>
-    <a href="https://huggingface.co/entity303"><img src="https://img.shields.io/badge/Hugging%20Face-entity303-green"></a>
+    <a href=""><img src="https://img.shields.io/badge/version-beta1.0-blue"></a>
-    <a href=""><img src="https://img.shields.io/badge/version-beta1.1-blue"></a>
     <a href=""><img src="https://img.shields.io/badge/os-Linux-9cf"></a>
     <a href=""><img src="https://img.shields.io/github/last-commit/pengxiao-song/lawgpt"></a>
     <a href="https://star-history.com/#pengxiao-song/LaWGPT&Timeline"><img src="https://img.shields.io/github/stars/pengxiao-song/lawgpt?color=yellow"></a> 
@@ -27,12 +26,6 @@ LaWGPT 是一系列基于中文法律知识的开源大语言模型。
 本项目持续开展，法律领域数据集及系列模型后续相继开源，敬请关注。
 
 ## 更新
-
-- 🌟 2023/05/30：公开发布
-  <a href="https://huggingface.co/entity303/lawgpt-lora-7b-v2"><img src="https://img.shields.io/badge/Model-LaWGPT--7B--beta1.1-yellow"></a>
- 
-  - **LaWGPT-7B-beta1.1**：法律对话模型，构造 35w 高质量法律问答数据集基于 Chinese-alpaca-plus-7B 指令精调
-
 - 📣 2023/05/26：开放 [Discussions 讨论区](https://github.com/pengxiao-song/LaWGPT/discussions)，欢迎朋友们交流探讨、提出意见、分享观点！
 
 - 🛠️ 2023/05/22：项目主分支结构调整，详见[项目结构](https://github.com/pengxiao-song/LaWGPT#项目结构)；支持[命令行批量推理](https://github.com/pengxiao-song/LaWGPT/blob/main/scripts/infer.sh)
@@ -40,15 +33,15 @@ LaWGPT 是一系列基于中文法律知识的开源大语言模型。
 - 🪴 2023/05/15：发布 [中文法律数据源汇总（Awesome Chinese Legal Resources）](https://github.com/pengxiao-song/awesome-chinese-legal-resources) 和 [法律领域词表](https://github.com/pengxiao-song/LaWGPT/blob/main/resources/legal_vocab.txt)
 
 - 🌟 2023/05/13：公开发布
-  <a href="https://huggingface.co/entity303/legal-lora-7b"><img src="https://img.shields.io/badge/Model-Legal--Base--7B-blue"></a> 
+  <a href=""><img src="https://img.shields.io/badge/Model-Legal--Base--7B-blue"></a> 
-  <a href="https://huggingface.co/entity303/lawgpt-legal-lora-7b"><img src="https://img.shields.io/badge/Model-LaWGPT--7B--beta1.0-yellow"></a>
+  <a href=""><img src="https://img.shields.io/badge/Model-LaWGPT--7B--beta1.0-yellow"></a>
   
   - **Legal-Base-7B**：法律基座模型，使用 50w 中文裁判文书数据二次预训练
   
   - **LaWGPT-7B-beta1.0**：法律对话模型，构造 30w 高质量法律问答数据集基于 Legal-Base-7B 指令精调
   
 - 🌟 2023/04/12：内部测试
-  <a href="https://huggingface.co/entity303/lawgpt-lora-7b"><img src="https://img.shields.io/badge/Model-Lawgpt--7B--alpha-yellow"></a>
+  <a href=""><img src="https://img.shields.io/badge/Model-Lawgpt--7B--alpha-yellow"></a>
   - **LaWGPT-7B-alpha**：在 Chinese-LLaMA-7B 的基础上直接构造 30w 法律问答数据集指令精调
 
 ## 快速开始
@@ -115,7 +108,7 @@ LaWGPT
 
 ## 数据构建
 
-本项目基于中文裁判文书网公开法律文书数据、司法考试数据等数据集展开，详情参考[中文法律数据源汇总（Awesome Chinese Legal Resources）](https://github.com/pengxiao-song/awesome-chinese-legal-resources)。
+本项目基于中文裁判文书网公开法律文书数据、司法考试数据等数据集展开，详情参考[中文法律数据汇总]()
 
 1. 初级数据生成：根据 [Stanford_alpaca](https://github.com/tatsu-lab/stanford_alpaca#data-generation-process) 和 [self-instruct](https://github.com/yizhongw/self-instruct) 方式生成对话问答数据
 2. 知识引导的数据生成：通过 Knowledge-based Self-Instruct 方式基于中文法律结构化知识生成数据。

infer.py

@@ -12,14 +12,13 @@ if torch.cuda.is_available():
     device = "cuda"
 
 
-class Infer():
+def main(
-    def __init__(
-        self,
     load_8bit: bool = False,
     base_model: str = "",
     lora_weights: str = "",
+    infer_data_path: str = "",
     prompt_template: str = "",  # The prompt template to use, will default to alpaca.
-    ):
+):
     prompter = Prompter(prompt_template)
     tokenizer = LlamaTokenizer.from_pretrained(base_model)
     model = LlamaForCausalLM.from_pretrained(
@@ -28,7 +27,6 @@ class Infer():
         torch_dtype=torch.float16,
         device_map="auto",
     )
-        
     try:
         print(f"Using lora {lora_weights}")
         model = PeftModel.from_pretrained(
@@ -38,7 +36,6 @@ class Infer():
         )
     except:
         print("*"*50, "\n Attention! No Lora Weights \n", "*"*50)
-            
     # unwind broken decapoda-research config
     model.config.pad_token_id = tokenizer.pad_token_id = 0  # unk
     model.config.bos_token_id = 1
@@ -51,14 +48,7 @@ class Infer():
     if torch.__version__ >= "2" and sys.platform != "win32":
         model = torch.compile(model)
 
-        self.base_model = base_model
+    def evaluate(
-        self.lora_weights = lora_weights
-        self.model = model
-        self.prompter = prompter
-        self.tokenizer = tokenizer
-
-    def generate_output(
-        self,
         instruction,
         input=None,
         temperature=0.1,
@@ -68,8 +58,8 @@ class Infer():
         max_new_tokens=256,
         **kwargs,
     ):
-        prompt = self.prompter.generate_prompt(instruction, input)
+        prompt = prompter.generate_prompt(instruction, input)
-        inputs = self.tokenizer(prompt, return_tensors="pt")
+        inputs = tokenizer(prompt, return_tensors="pt")
         input_ids = inputs["input_ids"].to(device)
         generation_config = GenerationConfig(
             temperature=temperature,
@@ -80,7 +70,7 @@ class Infer():
             **kwargs,
         )
         with torch.no_grad():
-            generation_output = self.model.generate(
+            generation_output = model.generate(
                 input_ids=input_ids,
                 generation_config=generation_config,
                 return_dict_in_generate=True,
@@ -88,47 +78,32 @@ class Infer():
                 max_new_tokens=max_new_tokens,
             )
         s = generation_output.sequences[0]
-        output = self.tokenizer.decode(s)
+        output = tokenizer.decode(s)
-        return self.prompter.get_response(output)
+        return prompter.get_response(output)
 
-    def infer_from_file(self, infer_data_path):
+    def infer_from_file():
         with open(infer_data_path) as f:
             for line in f:
                 data = json.loads(line)
                 instruction = data["instruction"]
                 output = data["output"]
                 print('=' * 100)
-                print(f"Base Model: {self.base_model}    Lora Weights: {self.lora_weights}")
+                print(f"Base Model: {base_model}    Lora Weights: {lora_weights}")
                 print("Instruction:\n", instruction)
-                model_output = self.generate_output(instruction)
+                model_output = evaluate(instruction)
                 print("Model Output:\n", model_output)
                 print("Ground Truth:\n", output)
                 print('=' * 100)
 
-
-def main(
-    load_8bit: bool = False,
-    base_model: str = "",
-    lora_weights: str = "",
-    prompt_template: str = "",  # The prompt template to use, will default to alpaca.
-    infer_data_path: str = "",
-):
-    infer = Infer(
-        load_8bit=load_8bit,
-        base_model=base_model,
-        lora_weights=lora_weights,
-        prompt_template=prompt_template
-    )
-    
     try:
-        infer.infer_from_file(infer_data_path)
+        infer_from_file()
-    except Exception as e:
+    except:
-        print(e, "Read infer_data_path Failed! Now Interactive Mode: ")
+        print("Read infer_data_path Failed! Now Interactive Mode: ")
         while True:
             print('=' * 100)
             instruction = input("请输入您的问题: ")
             print("LaWGPT:")
-            print(infer.generate_output(instruction))
+            print(evaluate(instruction))
             print('=' * 100)
 
 

requirements.txt

@@ -10,5 +10,3 @@ git+https://github.com/huggingface/transformers.git
 gradio
 sentencepiece
 wandb
-scipy
-socksio

scripts/infer.sh

@@ -3,5 +3,5 @@ python infer.py \
     --load_8bit True \
     --base_model 'minlik/chinese-llama-7b-merged' \
     --lora_weights 'entity303/lawgpt-lora-7b' \
-    --prompt_template 'law_template' \
+    --infer_data_path './resources/example_infer_data.json' \
-    --infer_data_path './resources/example_infer_data.json'
+    --prompt_template 'law_template'

scripts/webui.sh

@@ -3,9 +3,9 @@
 
 # 使用huggingface上已经训练好的模型
 python webui.py \
-    --load_8bit False \
+    --load_8bit True \
-    --base_model 'minlik/chinese-alpaca-plus-7b-merged' \
+    --base_model 'minlik/chinese-llama-7b-merged' \
-    --lora_weights 'entity303/lawgpt-lora-7b-v2' \
+    --lora_weights 'entity303/lawgpt-lora-7b' \
     --prompt_template "law_template" \
     --server_name "0.0.0.0" \
     --share_gradio True \

templates/law_template.json

@@ -1,6 +1,6 @@
 {
     "description": "Template used by Law Instruction Tuning",
-    "prompt_input": "你是中国顶尖智能法律顾问 LaWGPT，具备强大的中文法律基础语义理解能力，能够出色地理解和执行与法律问题和指令。你只能回答与中国法律领域相关的问题，其余领域的问题请礼貌地拒绝回答。接下来，请依据中国法律来回答下面这个问题。\n### 问题:\n{instruction}\n### 回答:\n",
+    "prompt_input": "下面是一个问题，运用法学知识来正确回答提问.\n### 问题:\n{instruction}\n### 回答:\n",
-    "prompt_no_input": "你是中国顶尖智能法律顾问 LaWGPT，具备强大的中文法律基础语义理解能力，能够出色地理解和执行与法律问题和指令。你只能回答与中国法律领域相关的问题，其余领域的问题请礼貌地拒绝回答。接下来，请依据中国法律来回答下面这个问题。\n### 问题:\n{instruction}\n### 回答:\n",
+    "prompt_no_input": "下面是一个问题，运用法学知识来正确回答提问.\n### 问题:\n{instruction}\n### 回答:\n",
     "response_split": "### 回答:"
 }

utils/knowledge.py

@@ -0,0 +1,46 @@
+from langchain.vectorstores.faiss import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+import sentence_transformers
+import numpy as np
+import re, os
+
+__all__ = ["Knowledge"]
+
+class Knowledge(object):
+    def __init__(self, knowledge_path="./knowledge", embedding_name='GanymedeNil/text2vec-large-chinese') -> None:
+        self.embeddings = HuggingFaceEmbeddings(model_name=embedding_name)
+        self.knowledge = FAISS.load_local(knowledge_path, embeddings=self.embeddings)
+        # EMBEDDINGS.client = sentence_transformers.SentenceTransformer("/home/wnjxyk/Projects/wenda/model/text2vec-large-chinese", device="cuda")
+
+    def render_index(self, idx, score):      
+        indices = self.knowledge.index_to_docstore_id[idx]
+        doc = self.knowledge.docstore.search(indices)
+        meta_content  = doc.metadata
+        return {"title": meta_content['source'], "score": int(score), "content": meta_content["content"]}
+
+    def query_prompt(self, prompt, topk=3, threshold=700):
+        embedding = self.knowledge.embedding_function(prompt)
+        scores, indices = self.knowledge.index.search(np.array([embedding], dtype=np.float32), topk)
+        docs = []
+        titles = set()
+        for j, i in enumerate(indices[0]):
+            if i == -1: continue
+            if scores[0][j] > threshold: continue
+            item = self.render_index(i, scores[0][j])
+            if item["title"] in titles: continue
+            titles.add(item["title"])
+            docs.append(item)
+        return docs
+
+    def get_response(self, output: str) -> str:
+        first, res = True, ""
+        for doc in output:
+            if not first: res += "---\n"
+            res += doc["content"]
+            first = False
+        return res
+    
+# knowledge = Knowledge()
+# answer = knowledge.query_prompt("强奸男性犯法吗？")
+# print(answer)
+# print(knowledge.get_response(answer))

webui.py

@@ -10,6 +10,7 @@ from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer, Aut
 
 from utils.callbacks import Iteratorize, Stream
 from utils.prompter import Prompter
+from utils.knowledge import Knowledge
 
 if torch.cuda.is_available():
     device = "cuda"
@@ -37,6 +38,7 @@ def main(
     ), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
 
     prompter = Prompter(prompt_template)
+    knowledge = Knowledge()
     tokenizer = LlamaTokenizer.from_pretrained(base_model)
     if device == "cuda":
         model = LlamaForCausalLM.from_pretrained(
@@ -106,6 +108,7 @@ def main(
     ):
         input=None
         prompt = prompter.generate_prompt(instruction, input)
+        legals = knowledge.query_prompt(instruction)
         inputs = tokenizer(prompt, return_tensors="pt")
         input_ids = inputs["input_ids"].to(device)
         generation_config = GenerationConfig(
@@ -152,7 +155,7 @@ def main(
                     if output[-1] in [tokenizer.eos_token_id]:
                         break
 
-                    yield prompter.get_response(decoded_output)
+                    yield prompter.get_response(decoded_output), knowledge.get_response(legals)
             print(decoded_output)
             return  # early return for stream_output
 
@@ -168,7 +171,7 @@ def main(
         s = generation_output.sequences[0]
         output = tokenizer.decode(s)
         print(output)
-        yield prompter.get_response(output)
+        yield prompter.get_response(output), knowledge.get_response(legals)
 
     gr.Interface(
         fn=evaluate,
@@ -200,6 +203,9 @@ def main(
             gr.inputs.Textbox(
                 lines=8,
                 label="Output",
+            ),
+            gr.inputs.Textbox(
+                label="Legal Ground",
             )
         ],
         title="🦙🌲 LaWGPT",