实验一
实验目标：Qwen2+Ollama+LlamaIndex实现RAG应用
实验时长：15分钟
运行设备：Mac，CPU，GPU均可
环境安装：
pip install llama-index llama_index.llms.ollama llama-index-embeddings-huggingface modelscope
复制模型路径，创建名为“ModelFile”的meta文件，内容如下：
FROM /mnt/workspace/qwen2-7b-instruct-q5_k_m.gguf # set the temperature to 0.7 [higher is more creative, lower is more coherent] PARAMETER temperature 0.7 PARAMETER top_p 0.8 PARAMETER repeat_penalty 1.05 TEMPLATE """{{ if and .First .System }}<|im_start|>system {{ .System }}<|im_end|> {{ end }}<|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assistant {{ .Response }}""" # set the system message SYSTEM """ You are a helpful assistant. """
使用ollama create命令创建自定义模型并运行
ollama create myqwen2 --file ./ModelFile ollama run myqwen2
然后运行如下RAG代码：
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings from llama_index.core.embeddings import resolve_embed_model from llama_index.llms.ollama import Ollama import sys # load doc qwen2 readme documents = SimpleDirectoryReader("/mnt/workspace/content").load_data() # bge embedding model Settings.embed_model = resolve_embed_model("local:/mnt/workspace/bge-base-en-v1.5") # ollama Settings.llm = Ollama(model="myqwen2", request_timeout=30.0) # create index index = VectorStoreIndex.from_documents(documents) # Either way we can now query the index query_engine = index.as_query_engine() response = query_engine.query("What is the maximum context length supported by Qwen2?") print(response)
Output
实验二
实验目标：Qwen2+Ollama+Langchain实现Agent应用
实验时长：15分钟
运行设备：Mac，CPU，GPU均可
环境安装：
pip install langchain_openai langchain langchain_experimental
模型下载：
使用modelscope-cli下载qwen2模型：
modelscope download --model=qwen/Qwen2-7B-Instruct-GGUF --local_dir . qwen2-7b-instruct-q5_k_m.gguf
运行Qwen2(本地文件，也可以直接用ollama hub)
复制模型路径，创建名为“ModelFile”的meta文件，内容如下：
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent from langchain_openai import ChatOpenAI import pandas as pd # 下载csv文件 df = pd.read_csv( "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv" ) agent = create_pandas_dataframe_agent( ChatOpenAI(api_key='ollama', # ollama 不需要使用真实的API key base_url = 'http://127.0.0.1:11434/v1', model="myqwen2"), df, verbose=True, allow_dangerous_code=True ) agent.invoke("how many rows are there?")
使用ollama create命令创建自定义模型并运行
ollama create myqwen2 --file ./ModelFile ollama run myqwen2
运行如下function call 代码（代码解释器）：
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent from langchain_openai import ChatOpenAI import pandas as pd # 下载csv文件 df = pd.read_csv( "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv" ) agent = create_pandas_dataframe_agent( ChatOpenAI(api_key='ollama', # ollama 不需要使用真实的API key base_url = 'http://127.0.0.1:11434/v1', model="myqwen2"), df, verbose=True, allow_dangerous_code=True ) agent.invoke("how many rows are there?")
Output
