LazyLLM

  export LAZYLLM_SILICONFLOW_API_KEY=<申请到的api key>

    import lazyllm
    from lazyllm import OnlineChatModule,WebModule
    api_key = 'sk-' #替换成申请的api
    # # 测试chat模块
    llm = OnlineChatModule(source='siliconflow', api_key=api_key, stream=False)
    w = WebModule(llm, port=8846, title="siliconflow")
    w.start().wait()

    import lazyllm
    from lazyllm import OnlineChatModule
    api_key = 'sk-' #替换成申请的api
    llm = OnlineChatModule(source='siliconflow', api_key=api_key,
    model='Qwen/Qwen2.5-VL-72B-Instruct')
    print(llm('你好，这是什么？', lazyllm_files=['your_picture.png']))

    import lazyllm
    from lazyllm import OnlineMultiModalModule
    api_key = 'sk-xxx'
    # 测试文生图 fuction=text2image
    llm =OnlineMultiModalModule(source='siliconflow',api_key=api_key,function='text2image')
    print(llm("生成一个可爱的小狗"))
    #测试文生语音 function=tts
    llm = OnlineMultiModalModule(source='siliconflow',api_key=api_key,function='tts')
    print(llm("你好，你叫什么名字",voice='fnlp/MOSS-TTSD-v0.5:anna'))

    import lazyllm
    from lazyllm import OnlineEmbeddingModule
    api_key = 'sk-'
    
    #测试embed模块
    llm = OnlineEmbeddingModule(source='siliconflow', api_key=api_key)
    print(llm("苹果"))
    
    #测试rerank模块
    llm = OnlineEmbeddingModule(source='siliconflow', api_key=api_key, type='rerank')
    print(llm(["苹果", ['苹果','香蕉','橘子']]))

    [-0.0024823144, -0.0075530247, -0.013154144, -0.031351723, -0.024489744, 0.009692847, 0.008086464, -0.037946977, 0.013251133, -0.046675995, -0.011390155, -0.011111312, 0.016779112, 0.054168403, 0.04849454, 0.014742341, 0.02341074, -0.015542501, 0.059939254, -0.024223024, 0.0065467632, -0.041244607, -0.022925794, -0.024804957, 0.006752865, -0.047548898, -0.03685585, 0.0513557....，-0.070656545, -0.01997975, 0.023398615, 0.008735079]

    [{'index': 0, 'relevance_score': 0.9946065545082092}, {'index': 2, 'relevance_score': 0.014802767895162106}, {'index': 1, 'relevance_score': 0.0004139931406825781}]

    import lazyllm
    api_key='sk-'
    embed_model = lazyllm.OnlineEmbeddingModule(source="siliconflow", api_key=api_key)
    documents = lazyllm.Document(
    dataset_path = "database",
    embed = embed_model
    )

    import lazyllm
    from lazyllm.tools import Retriever, Document, SentenceSplitter
    api_key='sk-'
    embed_model = lazyllm.OnlineEmbeddingModule(source="siliconflow", api_key=api_key)

    documents = Document(dataset_path='database', embed=embed_model, manager=False)
    rm = Retriever(documents, group_name='CoarseChunk', similarity='bm25', similarity_cut_off=0.01, topk=6)
    rm.start()
    print(rm("user query"))

    import lazyllm

    from lazyllm import (
    OnlineEmbeddingModule, OnlineChatModule, Document, SentenceSplitter,
    Retriever, Reranker, ChatPrompter, pipeline
    )
    # 初始化api key和提示词
    api_key = 'sk-'
    prompt = """
    You will play the role of an AI Q&A assistant and complete a dialogue task.
    In this task, you need to provide your answer based on the given context and question.
    """
    # 初始化模型
    embed_model = OnlineEmbeddingModule(source="siliconflow", api_key=api_key)
    rerank_model = OnlineEmbeddingModule(source="siliconflow", api_key=api_key, type="rerank")
    llm = OnlineChatModule(source="siliconflow", api_key=api_key)
    # 定义文档管理模块，并创建节点组
    doc = Document(dataset_path="/home/xxx/database", manager=False, embed=embed_model)
    doc.create_node_group(name="block", transform=SentenceSplitter, chunk_size=1024, chunk_overlap=100)
    doc.create_node_group(name="line", transform=SentenceSplitter, chunk_size=128, chunk_overlap=20, parent="block")
    # 构建RAG pipeline（多路召回--重排--提示词拼接--大模型回答）
    with pipeline() as ppl:
    with lazyllm.parallel().sum as ppl.prl:
        prl.r1 = Retriever(doc, group_name='line', similarity="cosine", topk=6, target='block')
        prl.r2 = Retriever(doc, group_name='block', similarity="cosine", topk=6)
    ppl.reranker = Reranker('ModuleReranker', model=rerank_model, output_format='content',
                            join=True) | bind(query=ppl.input)
    ppl.formatter = (lambda context, query: dict(context_str=str(context), query=query)) | bind(query=ppl.input)
    ppl.llm = llm.prompt(lazyllm.ChatPrompter(prompt, extra_keys=["context_str"]))
    ppl.start()
    query = "何为天道"
    
    print(ppl(query))