LLM之基于llama-index部署当地embedding与GLM-4模子并开端搭建RAG（其他大
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：先阐明，随着官方的变动，代码也大概变动，各人运行不起来，可以进官网查查资料
加载当地embedding模子
假如没有找到 llama_index.embeddings.huggingface
那么：pip install llama_index-embeddings-huggingface
还不可进入官网，输入huggingface举行搜索
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'
) 加载当地LLM模子
还是那句话，假如以下代码不可，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, 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)
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)
for token in response:
yield CompletionResponse(text=token, delta=token)
基于当地模子搭建简易RAG
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)
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)
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
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,
) 欢迎各人点赞或收藏
各人的点赞或收藏可以鼓励作者加快更新哟~
作者也新建了一个学习交换群，欢迎各人加入一起学习成长
通过链接获取二维码
链接：https://pan.baidu.com/s/1ZZZZ-ANJvMFHlanl-tbtew?pwd=9el1
参加链接：
LlamaIndex中的CustomLLM（当地加载模子）
llamaIndex 基于GPU加载当地embedding模子
官网文档
官网_starter_example_loca
官网_usage_custom
免责声明：如果侵犯了您的权益，请联系站长，我们会及时删除侵权内容，谢谢合作！更多信息从访问主页：qidao123.com:ToB企服之家，中国第一个企服评测及商务社交产业平台。
页:
[1]