Modular Diffusion— Python library for designing and training your own Diffusion Models with PyTorch. SapientML— Generative AutoML for Tabular Data. LLM Accuracy Enhancements AutoChain— AutoChain: Build light
The library API is designed to access the ollama REST API with functions like chat, generate, list, show, create, copy, delete, pull, push, and embeddings. Applications in Engineering The ollama python library facilitates LLMs in applications such as chatbots, customer support agents, and ...
LangGraph: Build Stateful AI Agents in Python Take this quiz to test your understanding of LangGraph, a Python library designed for stateful, cyclic, and multi-actor Large Language Model (LLM) applications. By working through this quiz, you'll revisit how to build LLM workflows and agents ...
🤖 Tiny Agents 上线 Python 版本啦!让 LLM 真正动起来,只需 70 行代码! Tiny Agents 是基于 MCP(Model Context Protocol)构建的轻量级智能体框架,现已支持 Python,并集成进 huggingface_hub,开发者可以轻松构建能“自动调用工具”的 LLM 应用⚙️ ✨ 有哪些亮点? 🧠 基于开放协议 MCP,标准化 LLM 与...
A Python library powered by Language Models (LLMs) for conversational data discovery and analysis. Topics python docker data-science ai pandas gemini data-analysis mistral ai-agents pinecone groq vector-database openai-api llm anthropic vllm ollama Resources Readme License MIT license Activity...
python-user-agents,浏览器的用户代理(user-agents)的解析器。sqlparse,SQL解析器。pygments,一个通用的语法高亮工具。python-nameparser,解析人名,分解为单独的成分。pyparsing,通用解析器生成框架。tablib,表格数据格式,包括,XLS、CSV,JSON,YAML。python-docx,docx文档读取,查询和修改,微软Word 2007 / 2008的docx文件...
from typing import Optional, List from llm_sandbox import SandboxSession from langchain import hub from langchain_openai import ChatOpenAI from langchain.tools import tool from langchain.agents import AgentExecutor, create_tool_calling_agent @tool def run_code(lang: str, code: str, libraries: ...
LangChain Python agents. As noted above, each component in this flow is a Python code component. When you run a flow on DataStax Langflow, it runs on our cloud infrastructure. Chains. Chains are workflows that link together multiple components that perform complex tasks (like data ingestion ...
This post shares practical tips for using LLMs to write code effectively, emphasizing that it's not always easy and requires managing context, setting expectations, and thorough testing. The author suggests thinking of LLMs as over-confident, lightning-fast pair programming assistants and provides ...
choice(USER_AGENTS)} count = 1 # 初始化URL计数器 for url in urls: details = { 'fund_name': fetch_fund_details(url, '//*[@id="body"]//div[@class="fundDetail-tit"]/div/text()', headers), 'fund_type': fetch_fund_details(url, '//*[@id="body"]//div[@class="infoOfFund"...