LLM之基于llama-index部署本地embedding与GLM-4模型并初步搭建RAG（其他大模型也可，附上ollma方式运行）

黑夜寻白天2024-08-23 22:40

前言

日常没空，留着以后写

llama-index简介

官网：https://docs.llamaindex.ai/en/stable/

简介也没空，以后再写

注：先说明，随着官方的变动，代码也可能变动，大家运行不起来，可以进官网查查资料

加载本地embedding模型

如果没有找到 llama_index.embeddings.huggingface

那么：pip install llama_index-embeddings-huggingface

还不行进入官网，输入huggingface进行搜索

python 复制代码 
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings

Settings.embed_model = HuggingFaceEmbedding(
    model_name=f"{embed_model_path}",device='cuda'

)

加载本地LLM模型

还是那句话，如果以下代码不行，进官网搜索Custom LLM Model

python 复制代码 
from llama_index.core.llms import (
    CustomLLM,
    CompletionResponse,
    CompletionResponseGen,
    LLMMetadata,
)
from llama_index.core.llms.callbacks import llm_completion_callback
from transformers import AutoTokenizer, AutoModelForCausalLM

class GLMCustomLLM(CustomLLM):
    context_window: int = 8192  # 上下文窗口大小
    num_output: int = 8000  # 输出的token数量
    model_name: str = "glm-4-9b-chat"  # 模型名称
    tokenizer: object = None  # 分词器
    model: object = None  # 模型
    dummy_response: str = "My response"

    def __init__(self, pretrained_model_name_or_path):
        super().__init__()

        # GPU方式加载模型
        self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, device_map="cuda", trust_remote_code=True)
        self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="cuda", trust_remote_code=True).eval()

        # CPU方式加载模型
        # self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, device_map="cpu", trust_remote_code=True)
        # self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="cpu", trust_remote_code=True)
        self.model = self.model.float()

    @property
    def metadata(self) -> LLMMetadata:
        """Get LLM metadata."""
        # 得到LLM的元数据
        return LLMMetadata(
            context_window=self.context_window,
            num_output=self.num_output,
            model_name=self.model_name,
        )

    # @llm_completion_callback()
    # def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
    #     return CompletionResponse(text=self.dummy_response)
    #
    # @llm_completion_callback()
    # def stream_complete(
    #     self, prompt: str, **kwargs: Any
    # ) -> CompletionResponseGen:
    #     response = ""
    #     for token in self.dummy_response:
    #         response += token
    #         yield CompletionResponse(text=response, delta=token)

    @llm_completion_callback()  # 回调函数
    def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
        # 完成函数
        print("完成函数")

        inputs = self.tokenizer.encode(prompt, return_tensors='pt').cuda()  # GPU方式
        # inputs = self.tokenizer.encode(prompt, return_tensors='pt')  # CPU方式
        outputs = self.model.generate(inputs, max_length=self.num_output)
        response = self.tokenizer.decode(outputs[0])
        return CompletionResponse(text=response)

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, **kwargs: Any
    ) -> CompletionResponseGen:
        # 流式完成函数
        print("流式完成函数")

        inputs = self.tokenizer.encode(prompt, return_tensors='pt').cuda()  # GPU方式
        # inputs = self.tokenizer.encode(prompt, return_tensors='pt')  # CPU方式
        outputs = self.model.generate(inputs, max_length=self.num_output)
        response = self.tokenizer.decode(outputs[0])
        for token in response:
            yield CompletionResponse(text=token, delta=token)

基于本地模型搭建简易RAG

python 复制代码 
from typing import Any

from llama_index.core.llms import (
    CustomLLM,
    CompletionResponse,
    CompletionResponseGen,
    LLMMetadata,
)
from llama_index.core.llms.callbacks import llm_completion_callback
from transformers import AutoTokenizer, AutoModelForCausalLM
from llama_index.core import Settings,VectorStoreIndex,SimpleDirectoryReader
from llama_index.embeddings.huggingface import HuggingFaceEmbedding


class GLMCustomLLM(CustomLLM):
    context_window: int = 8192  # 上下文窗口大小
    num_output: int = 8000  # 输出的token数量
    model_name: str = "glm-4-9b-chat"  # 模型名称
    tokenizer: object = None  # 分词器
    model: object = None  # 模型
    dummy_response: str = "My response"

    def __init__(self, pretrained_model_name_or_path):
        super().__init__()

        # GPU方式加载模型
        self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, device_map="cuda", trust_remote_code=True)
        self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="cuda", trust_remote_code=True).eval()

        # CPU方式加载模型
        # self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path, device_map="cpu", trust_remote_code=True)
        # self.model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, device_map="cpu", trust_remote_code=True)
        self.model = self.model.float()

    @property
    def metadata(self) -> LLMMetadata:
        """Get LLM metadata."""
        # 得到LLM的元数据
        return LLMMetadata(
            context_window=self.context_window,
            num_output=self.num_output,
            model_name=self.model_name,
        )

    # @llm_completion_callback()
    # def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
    #     return CompletionResponse(text=self.dummy_response)
    #
    # @llm_completion_callback()
    # def stream_complete(
    #     self, prompt: str, **kwargs: Any
    # ) -> CompletionResponseGen:
    #     response = ""
    #     for token in self.dummy_response:
    #         response += token
    #         yield CompletionResponse(text=response, delta=token)

    @llm_completion_callback()  # 回调函数
    def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
        # 完成函数
        print("完成函数")

        inputs = self.tokenizer.encode(prompt, return_tensors='pt').cuda()  # GPU方式
        # inputs = self.tokenizer.encode(prompt, return_tensors='pt')  # CPU方式
        outputs = self.model.generate(inputs, max_length=self.num_output)
        response = self.tokenizer.decode(outputs[0])
        return CompletionResponse(text=response)

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, **kwargs: Any
    ) -> CompletionResponseGen:
        # 流式完成函数
        print("流式完成函数")

        inputs = self.tokenizer.encode(prompt, return_tensors='pt').cuda()  # GPU方式
        # inputs = self.tokenizer.encode(prompt, return_tensors='pt')  # CPU方式
        outputs = self.model.generate(inputs, max_length=self.num_output)
        response = self.tokenizer.decode(outputs[0])
        for token in response:
            yield CompletionResponse(text=token, delta=token)


if __name__ == "__main__":


    # 定义你的LLM
    pretrained_model_name_or_path = r'/home/nlp/model/LLM/THUDM/glm-4-9b-chat'
    embed_model_path = '/home/nlp/model/Embedding/BAAI/bge-m3'

    Settings.embed_model = HuggingFaceEmbedding(
        model_name=f"{embed_model_path}",device='cuda'

    )

    Settings.llm = GLMCustomLLM(pretrained_model_name_or_path)

    documents = SimpleDirectoryReader(input_dir="home/xxxx/input").load_data()
    index = VectorStoreIndex.from_documents(
        documents,
    )


    # 查询和打印结果
    query_engine = index.as_query_engine()
    response = query_engine.query("萧炎的表妹是谁?")

    print(response)

ollama

python 复制代码 
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.ollama import Ollama

documents = SimpleDirectoryReader("data").load_data()

# bge-base embedding model
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")

# ollama
Settings.llm = Ollama(model="llama3", request_timeout=360.0)

index = VectorStoreIndex.from_documents(
    documents,
)

欢迎大家点赞或收藏

大家的点赞或收藏可以鼓励作者加快更新哟~

参加链接：

LlamaIndex中的CustomLLM（本地加载模型）
llamaIndex 基于GPU加载本地embedding模型

官网文档

官网_starter_example_loca

官网_usage_custom

相关推荐
badhope3387 小时前
2026 年大模型热门教程盘点：从 Prompt、RAG 到 Agent，到底该先学什么
学习路线·rag·ai agent·大模型应用开发·ai教程
XLYcmy18 小时前
AI 时代，MOM（制造运营管理系统）该如何演进？ 中
ai·llm·agent·工作流·rag·harness·工业系统
Java的搬运工19 小时前
生产级 RAG 检索调优：BM25 + 向量混合检索与 Cross-Encoder 重排序 (Rerank)
springboot3·rag·bm25·rrf·bge·混合检索·reranker
91刘仁德1 天前
RAG实战-Embedding模型（BGE）
笔记·embedding·rag
像风一样自由20201 天前
42.VueReactNextjs如何为AI应用设计前端交互
前端·人工智能·大模型·交互·rag·智能体
像风一样自由20201 天前
41.用FastAPI搭建一个RAG后端需要哪些接口
人工智能·大模型·fastapi·rag·智能体
無a伟2 天前
RAG演进路线与核心架构
agent·rag
Steve__evetS2 天前
RAG流程
ai·rag
AI-Frontiers2 天前
一文看懂 RAGFlow 四层存储：元数据、对象、检索、缓存如何协作
rag
热门推荐
01国内可直接用、免费额度/永久免费的大模型API清单（含 SiliconFlow、火山、阿里、智谱、百度、Kimi、DeepSeek、DMXAPI 等）02历年考研数学一、数学二、数学三真题试卷及答案PDF03Telegram APK 下载安装完整指南 — 2026年最新04GitHub 镜像站点05蜘蛛磁力 搜索引擎大全，如何使用蜘蛛磁力查找磁力链接062026 爱知・名古屋亚运会怎么看？直播平台+赛程安排分享07微信历史版本含下载地址（ Windows PC | 安卓 | MAC ）及设置微信不更新08jev-ultrafast 深度解析：7 秒订机票的浏览器 Agent 是如何炼成的09给AI一张“业务概念地图“：本体如何从哲学走向企业智能102026 年 9 月国内外主流大模型横向硬核评测：价格、算力、业务场景全维度对比，GPT‑6 Astra横空出世