具体再看init_cfg这个函数是在langchain-ChatGLM/chains/local_doc_qa.py中实现的 def init_cfg(self, embedding_model: str = EMBEDDING_MODEL, embedding_device=EMBEDDING_DEVICE, llm_model: BaseAnswer = None, top_k=VECTOR_SEARCH_TOP_K, ): self.llm = llm_model self.embeddings = HuggingFaceEmbedd...
Lib\site-packages\langchain_community\embeddings\huggingface.py", line 153, in __init__ self.client = INSTRUCTOR( ^^^ File "D:\Scripts\ChromaDB-Plugin-for-LM-Studio\v4_3 - working\Lib\site-packages\sentence_transformers\SentenceTransformer.py", line 191, in __init__ modules = self._loa...
在使用LangChain打造自己GPT的过程中,大家可能已经意识到这里的关键是根据Query进行语义检索找到最相关的TOP Documents,语义检索的重要前提是Sentence Embeddings。可惜目前看到的绝大部分材料都是使用OpenAIEmbeddings(em... 可能跟OpenAI的官方CookBook的样例比较相关)。 为什么需要一个本地的Sentence Embeddings? 虽然OpenAI ...
I using sentence transformers for have some langchain hugging face embeddings and when I try to dockerize the application it is going above 8gb the reason behind this is sentence trabsformers which is installing Nvidia gpu supported packages, Now how do I get sentence transformers only for CPU...
join above two steps using module argument and pass it to sentenceTransformer Let’s put this to code: # Define model ## Step 1: use an existing language model word_embedding_model = models.Transformer('bert-base-uncased') ## Step 2: use a pool function over the token embeddings ...
decoder是只有一层的transformer; mask 的比例,在encoder中是 15到30%,decoder 中是 50%到70%; BGE模型一共有3个版本:small (24M), base (102M), and large (326M)。 BGE 对应的论文是:C-Pack: Packed Resources For General Chinese Embeddings (arxiv.org) 代码:FlagEmbedding/FlagEmbedding/baai_genera...
fromtypingimportListimporttorchfromlangchain.embeddingsimportHuggingFaceEmbeddingsclassEmbeddingsModel:def__init__(self):self.model_name="sentence-transformers/multi-qa-MiniLM-L6-cos-v1"encode_kwargs={"normalize_embeddings":True}iftorch.cuda.is_available():# Use CUDA GPUdevice=torch.device("cuda:0"...
在当前案例中,这不是必要的操作。但当加载多页PDF文档时就需要先把它们合并为一个文本。这一点貌似与langchain不一样,在langchain中加载多页的PDF文档并不需要先合并为一个文本。 创建Sentence Window Retriever 首先,考虑如何创建将文档分解为各个句子的SentenceWindowNodeParser,然后能够在允许的窗口内为每个句子添加...
embeddings import HuggingFaceEmbeddings from langchain.text_splitter import RecursiveCharacterTextSplitter from sentence_transformers import SentenceTransformer, util import numpy as np import torch import time import pynvml import gc from datasets import load_dataset def cleanup(model_instance=None): if ...
A large disadvantage of BERT network structure is that no independent sentence embeddings are computed, which makes it difficult to derive sentence embeddings from BERT. The standard sentence methods do not adequately address some linguistic properties, which are important factors for producing appropriate...