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）

本文主要是介绍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），希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

前言

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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

from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import SettingsSettings.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

from llama_index.core.llms import (CustomLLM,CompletionResponse,CompletionResponseGen,LLMMetadata,
)
from llama_index.core.llms.callbacks import llm_completion_callback
from transformers import AutoTokenizer, AutoModelForCausalLMclass 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()@propertydef 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

from typing import Anyfrom 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 HuggingFaceEmbeddingclass 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()@propertydef 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LLMpretrained_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)

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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
 

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