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

Featured

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

• https://gaodalie.substack.com/p/langchain-mcp-rag-ollama-the-key
• https://python.langchain.com/docs/tutorials/rag/
• https://python.langchain.com/docs/tutorials/qa_chat_history/
• https://python.langchain.com/docs/tutorials/agents/

Start

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

Next, install pip deps.

pip install -r requirements.txt

Then, config env file.

cp sample.env .env # Modify key-values in .env

At last, run

./start.sh

Other tool calling enabled models

myaniu/qwen2.5-1m:7b
myaniu/qwen2.5-1m:14b