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.idea
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cache
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docs/zh_wikipedia
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## 🔥 效果演示
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## 🚀 特性
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* [ ] 检索结果过滤与排序
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* [ ] 互联网检索结果接入
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* [ ] 模型初始化有问题
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* [ ] 增加非LangChain策略
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## 交流
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欢迎多提建议、Bad cases,目前尚不完善,欢迎进群及时交流,也欢迎大家多提PR
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#!/usr/bin/env python
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# -*- coding:utf-8 _*-
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"""
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@author:quincy qiang
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@license: Apache Licence
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@file: create_knowledge.py
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@time: 2023/04/18
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@contact: yanqiangmiffy@gamil.com
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@software: PyCharm
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@description: coding..
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"""
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from langchain.docstore.document import Document
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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from tqdm import tqdm
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# 中文Wikipedia数据导入示例:
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embedding_model_name = '/home/searchgpt/pretrained_models/ernie-gram-zh'
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docs_path = '/home/searchgpt/yq/Knowledge-ChatGLM/docs'
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embeddings = HuggingFaceEmbeddings(model_name=embedding_model_name)
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docs = []
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with open('docs/zh_wikipedia/zhwiki.sim.utf8', 'r', encoding='utf-8') as f:
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for idx, line in tqdm(enumerate(f.readlines())):
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metadata = {"source": f'doc_id_{idx}'}
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docs.append(Document(page_content=line.strip(), metadata=metadata))
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vector_store = FAISS.from_documents(docs, embeddings)
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vector_store.save_local('cache/zh_wikipedia/')
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34
main.py
34
main.py
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# 修改成自己的配置!!!
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class LangChainCFG:
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llm_model_name = 'THUDM/chatglm-6b-int4-qe' # 本地模型文件 or huggingface远程仓库
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embedding_model_name = 'GanymedeNil/text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库
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llm_model_name = '../../pretrained_models/chatglm-6b-int4-qe' # 本地模型文件 or huggingface远程仓库
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embedding_model_name = '../../pretrained_models/text2vec-large-chinese' # 检索模型文件 or huggingface远程仓库
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vector_store_path = './cache'
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docs_path = './docs'
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label="large language model",
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value="ChatGLM-6B-int4")
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with gr.Tab("select"):
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selectFile = gr.Dropdown(file_list,
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label="content file",
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interactive=True,
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value=file_list[0] if len(file_list) > 0 else None)
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with gr.Tab("upload"):
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file = gr.File(label="请上传知识库文件",
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top_k = gr.Slider(1,
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20,
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value=2,
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step=1,
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label="向量匹配 top k",
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interactive=True)
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kg_name = gr.Radio(['中文维基百科', '百度百科数据', '坦克世界'],
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label="知识库",
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value='中文维基百科',
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interactive=True)
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file = gr.File(label="将文件上传到数据库",
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visible=True,
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file_types=['.txt', '.md', '.docx', '.pdf']
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)
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file.upload(upload_file,
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inputs=file,
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outputs=selectFile)
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outputs=None)
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with gr.Column(scale=4):
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with gr.Row():
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with gr.Column(scale=4):
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],
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outputs=[message, chatbot, state, search])
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demo.queue(concurrency_count=2).launch(server_name='0.0.0.0', server_port=8888, share=False,show_error=True, enable_queue=True)
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demo.queue(concurrency_count=2).launch(
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server_name='0.0.0.0',
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server_port=8888,
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share=False,
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show_error=True,
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debug=True,
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enable_queue=True
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)
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from duckduckgo_search.utils import SESSION
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SESSION.proxies = {
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"http": f"socks5h://localhost:7890",
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"https": f"socks5h://localhost:7890"
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}
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# SESSION.proxies = {
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# "http": f"socks5h://localhost:7890",
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# "https": f"socks5h://localhost:7890"
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# }
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r = ddg("马保国")
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print(r)
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@ -4,8 +4,8 @@ from langchain.document_loaders import UnstructuredFileLoader
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from langchain.embeddings.huggingface import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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embedding_model_name = 'pretrained_models/ernie-gram-zh'
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docs_path = 'docs'
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embedding_model_name = '/home/searchgpt/pretrained_models/ernie-gram-zh'
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docs_path = '/home/searchgpt/yq/Knowledge-ChatGLM/docs'
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embeddings = HuggingFaceEmbeddings(model_name=embedding_model_name)
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docs = []
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search_result = vector_store.similarity_search_with_score(query='科比', k=2)
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print(search_result)
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loader = UnstructuredFileLoader(f'{docs_path}/added/科比.txt', mode="elements")
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loader = UnstructuredFileLoader(f'{docs_path}/added/马保国.txt', mode="elements")
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doc = loader.load()
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vector_store.add_documents(doc)
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print(doc)
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