智能体开发,实现自定义知识库,基于 LangChain,qwen 7b, ollama, chatopera | LLMs

Agent built with LangChain, and Chatopera Cloud

By themselves, language models can't take actions - they just output text. A big use case for LangChain is creating agents. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs necessary to perform the action. After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. This is often achieved via tool-calling.

本教程示例程序:https://github.com/hailiang-wang/llm-get-started/tree/master/005_agent_langchain

Config custom knowledge base with Chatopera Cloud Services, https://bot.chatopera.com/

Leverge chat routes with LLMs - e.g. mistral-nemo, myaniu/qwen2.5-1m:7b, myaniu/qwen2.5-1m:14b.

RAG & Agent

Start

First, install ollama, next, run ollama pull mistral-nemo:latest, checkout mistral-nemo.

Next, install pip deps.

复制代码
pip install -r requirements.txt

Then, config env file.

复制代码
cp sample.env .env # Modify key-values in .env

At last, run

复制代码
./start.sh

Other tool calling enabled models

复制代码
myaniu/qwen2.5-1m:7b
myaniu/qwen2.5-1m:14b
相关推荐
deephub12 小时前
AI代理性能提升实战:LangChain+LangGraph内存管理与上下文优化完整指南
人工智能·深度学习·神经网络·langchain·大语言模型·rag
都叫我大帅哥17 小时前
LangChain分层记忆解决方案:完整案例
python·langchain
alex10018 小时前
AI Agent开发学习系列 - langchain之LCEL(5):如何创建一个Agent?
人工智能·python·语言模型·langchain·prompt·向量数据库·ai agent
青Cheng序员石头21 小时前
Prompt Engineering vs Vibe Coding vs Context Engineering
langchain·llm·aigc
数据智能老司机21 小时前
构建由 LLM 驱动的 Neo4j 应用程序——使用 Neo4j 和 Haystack 实现强大搜索功能
langchain·llm·aigc
都叫我大帅哥2 天前
🚀 LangGraph终极指南:从入门到生产级AI工作流编排
python·langchain
showyoui2 天前
LangChain vs LangGraph:从困惑到清晰的认知之路(扫盲篇)
langchain·ai编程
_一条咸鱼_2 天前
LangChain记忆序列化与持久化方案源码级分析(37)
人工智能·面试·langchain
数据智能老司机2 天前
构建由 LLM 驱动的 Neo4j 应用程序——揭开 RAG 的神秘面纱
langchain·llm·aigc
数据智能老司机2 天前
构建由 LLM 驱动的 Neo4j 应用程序——构建智能应用的知识图谱基础理解
langchain·llm·aigc