fromvllmimportLLM,SamplingParamsmodel=LLM("selfrag/selfrag_llama2_7b",download_dir="/gscratch/h2lab/akari/model_cache",dtype="half")sampling_params=SamplingParams(temperature=0.0,top_p=1.0,max_tokens=100,skip_special_tokens=False)defformat_prompt(input,paragraph=None):prompt="### Instruction:\...
b: float) -> float: return a * bmultiply_tool=FunctionTool.from_defaults(fn=multiply)defadd(a:float,b:float)->float:returna+badd_tool=FunctionTool.from_defaults(fn=add)agent=ReActAgent.from_tools([multiply_tool,add_tool],llm=llm,verbose=True) ...
from llmware.models import ModelCatalog ModelCatalog().get_llm_toolkit() # get all SLIM models, delivered as small, fast quantized tools ModelCatalog().tool_test_run("slim-sentiment-tool") # see the model in action with test script included ...
A Personal Assistant leveraging Retrieval-Augmented Generation (RAG) and the LLaMA-3.1-8B-Instant Large Language Model (LLM). This tool is designed to revolutionize PDF document analysis tasks by combining machine learning with retrieval-based systems. -
Generator: Acts like a writer, taking the prompt and information retrieved to create a response. We're using here a Large Language Model (LLM) for this task. When you ask a question to the chatbot, the RAG pipeline works like this: ...
SWE智能体基于AI技术开发,通过LLM和上下文感知技术,辅助或自主完成代码编写、调试等任务。 智能体技术则将融合同步(如VS Code等实时协作工具)和异步两种交互模式,以实现能力增强。 从而确保只需使用自然语言,发出执行任务的指令,智能体就能自主解析并完成操作步骤。 在当前的VS Code环境中,代理模式主要负责处理同步任务...
Web 应用或桌面工具栏 (Web APP or Desktop Toolbar):用户可以通过 Web 应用或桌面工具栏与系统进行交互,进行搜索、查看总结和指标等操作。 使用LLM 搜索历史 (Search history with LLM):利用大型语言模型 (LLM) 对历史记录进行更智能的搜索。 创建/查看摘要和指标 (Create/View summaries, metrics):系统能够生成...
A simple RAG system consists of five stages: text embedding model, LLM, information retrieval, ranking, and response generation. All of these steps rely onvector databases, so that's where we’ll start. To build a RAG-enabled pipeline, we first start bysetting up our vector database on As...
to meet users' needs. The chapter concludes with the implementation of an automated ranking system to enhance the generative model's performance, making it highly applicable to real-world business settings.In conclusion, RAG-Driven Generative AI is a must-read for anyone involved with LLMs. Rothm...
LLM Model Options Cloudera AI Inference (CAII) Setup: AWS Bedrock Setup: Azure OpenAI Setup: Document Storage Options: Enhanced Parsing Options: Cloudera DataFlow (Nifi) Setup: Updating RAG Studio Common Issues CDP Token Override Air-gapped Environments ...