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Langchain-Chatchat(原Langchain-ChatGLM)基于 Langchain 与 ChatGLM, Qwen 与 Llama 等语言模型的 RAG 与 Agent 应用 | Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge based LLM (like ChatGLM, Qwen and Llama) RAG and Agent app with langchain
Here is a simple Python program that can combine all the text files in the current directory: ``` import os for filename in os.listdir(): if filename.endswith('.txt'): with open(filename, 'a') as f: f.write('') ``` This program uses the `os` module to list all the files...
endswith('.pdf'): pdf_path = os.path.join(pdf_folder, filename) output_path = os.path.join(output_folder, filename.replace('.pdf', '.docx')) text = pdf_to_text(pdf_path) text_to_word(text, output_path) if __name__ == "__main__": pdf_folder_path = "path/to/pdf/folde...
if (exportcsv) { filename_root <- strsplit(filename, "\\.")[[1]][1] filename_with_winner <- paste0(filename_root, "_winners.csv") rio::export(data, filename_with_winner) }1.2.3.4.5.推荐的插件 复制 Use `paste()` instead of `paste0()` to ensure a space is included ...
{ // 复制代码运行请自行打印 API 的返回值 InitIMConnectResponse response = client.initIMConnectWithOptions(initIMConnectRequest, runtime); System.out.println(response.getBody().getData()); } catch (Exception error) { // 如有需要,请打印 error error.printStackTrace(); }//TeaException error =...
Flexible & Extensible: Attach tools like DALL-E-3, file search, code execution, and more Compatible with Custom Endpoints, OpenAI, Azure, Anthropic, AWS Bedrock, and more Model Context Protocol (MCP) Support for Tools Use LibreChat Agents and OpenAI Assistants with Files, Code Interpreter, Tool...
images_with_prompts(prompt, navigator_prompt, model_choice): return fake_gan(prompt) def select_model(model_choice): return fake_gan() def add_text(history, text): history = history + [(text, None)] return history, gr.Textbox(value="", interactive=False) def add_file(history, file):...
[--file_format 文件保存格式,默认是markdown的md格式,也可以是txt] parser.add_argument("--pdf_path", type=str, default='', help="if none, the bot will download from arxiv with query") parser.add_argument("--query", type=str, default='all: ChatGPT robot', help="the query string, ...
path.append('../..') from textgen.t5 import T5Model def load_data(file_path): data = [] with open(file_path, 'r', encoding='utf-8') as f: for line in f: line = line.strip('\n') terms = line.split('\t') if len(terms) == 2: data.append(['QA', terms[0], terms...