需要python环境

官网注册：（后续调用模型需要秘钥key）Try NVIDIA NIM APIs

可以看到有多种模型：

官方案例

1.安装相关依赖：

pip install langchain_nvidia_ai_endpoints langchain-community langchain-text-splitters faiss-cpu gradio==3.50.0 setuptools beautifulsoup4

2.官方案例：

# -*- coding: utf-8 -*-

# 导入必要的库
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import chain
import os
import gradio as gr
from datetime import datetime
# Even if you do not know the full answer, generate a one-paragraph hypothetical answer to the below question in Chinese
# 定义假设性回答模板
hyde_template = """Even if you do not know the full answer, generate a one-paragraph hypothetical answer to the below question:

{question}"""

# 定义最终回答模板
template = """Answer the question based only on the following context:
{context}

Question: {question}
"""

# 定义函数来处理问题
def process_question(url, api_key, model_name, question):
    # 初始化加载器并加载数据
    loader = WebBaseLoader(url)
    docs = loader.load()

    # 设置环境变量
    os.environ['NVIDIA_API_KEY'] = api_key

    # 初始化嵌入层
    embeddings = NVIDIAEmbeddings()
    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
    documents = text_splitter.split_documents(docs)
    vector = FAISS.from_documents(documents, embeddings)
    retriever = vector.as_retriever()

    # 初始化模型
    model = ChatNVIDIA(model=model_name)

    # 创建提示模板
    hyde_prompt = ChatPromptTemplate.from_template(hyde_template)
    hyde_query_transformer = hyde_prompt | model | StrOutputParser()

    # 定义检索函数
    @chain
    def hyde_retriever(question):
        hypothetical_document = hyde_query_transformer.invoke({"question": question})
        return retriever.invoke(hypothetical_document)

    # 定义最终回答链
    prompt = ChatPromptTemplate.from_template(template)
    answer_chain = prompt | model | StrOutputParser()

    @chain
    def final_chain(question):
        documents = hyde_retriever.invoke(question)
        response = ""
        for s in answer_chain.stream({"question": question, "context": documents}):
            response += s
        return response

    # 调用最终链获取答案
    return str(datetime.now())+final_chain.invoke(question)

# 定义可用的模型列表
models = ["mistralai/mistral-7b-instruct-v0.2","meta/llama-3.1-405b-instruct"]

# 启动Gradio应用
iface = gr.Interface(
    fn=process_question,
    inputs=[
        gr.Textbox(label="输入需要学习的网址"),
        gr.Textbox(label="NVIDIA API Key"),
        gr.Dropdown(models, label="选择语言模型"),
        gr.Textbox(label="输入问题")
    ],
    outputs="text",
    title="网页知识问答系统"
)

# 启动Gradio界面
iface.launch()

 3.获取Key：

4.运行效果： 

更多尝试

图像生成模型

使用的模型：

总结：

感受不错，模型效果非常不错，体验了AI模型在知识问答领域的实际应用，这有助于理解AI技术如何影响信息检索和内容生成。