File size: 4,008 Bytes
06c6776 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | import os
import shutil
import tempfile
import requests
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.blob_loaders import FileSystemBlobLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.vectorstores.utils import DistanceStrategy
from langchain.text_splitter import CharacterTextSplitter
os.makedirs('store', exist_ok = True)
def download(args: dict):
if not 'dir' in args:
raise ValueError('require dir')
if 'zip_url' in args:
res = requests.get(args['zip_url'])
with tempfile.NamedTemporaryFile(suffix=".zip") as t:
with open(t.name, 'wb') as f:
f.write(res.content)
if os.path.exists(f"store/{args['dir']}"):
shutil.rmtree(f"store/{args['dir']}")
shutil.unpack_archive(t.name, f"store/{args['dir']}")
elif 'url' in args:
os.makedirs(f"store/{args['dir']}", exist_ok=True)
res = requests.get(args['url'])
filepath = f"store/{args['dir']}/{os.path.basename(args['url'])}"
with open(filepath, 'wb') as f:
f.write(res.content)
elif 'text' in args:
os.makedirs(f"store/{args['dir']}", exist_ok=True)
filepath = f"store/{args['dir']}/text.txt"
with open(filepath, 'w', encoding='utf-8') as f:
f.write(args['text'])
def docs_load(args: dict):
loader = GenericLoader.from_filesystem(
path=f"store/{args['dir']}",
glob="**/[!.]*",
show_progress=True,
)
docs = loader.load()
return docs
def chunk_split(docs, chunk_size):
text_splitter = CharacterTextSplitter(
separator='\n\n',
chunk_size=chunk_size,
chunk_overlap=0,
length_function=len
)
chunk_docs = text_splitter.create_documents([doc.page_content for doc in docs])
return chunk_docs
def vector(docs, args: dict):
embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
vector_store = FAISS.from_documents(documents=docs,
embedding=embeddings,
distance_strategy=DistanceStrategy.MAX_INNER_PRODUCT,
normalize_L2=True)
return vector_store
def vector_save(docs, args: dict):
vector_store = vector(docs, args)
folder_path = f"store/{args['dir']}/vector"
vector_store.save_local(folder_path=folder_path)
return vector_store
def vector_load(args: dict):
folder_path = f"store/{args['dir']}/vector"
if not os.path.exists(folder_path):
raise ValueError(f"missing store/{args['dir']}/vector")
embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
vector_store = FAISS.load_local(folder_path=folder_path,
embeddings=embeddings,
distance_strategy=DistanceStrategy.MAX_INNER_PRODUCT,
normalize_L2=True)
return vector_store
def search(vector_store, args: dict):
results = vector_store.similarity_search_with_score(query=args['query'], k=args['k'])
detail = []
for r in results:
detail.append([r[0].page_content, float(r[1])])
return results[0][0].page_content, detail
def load_dirs():
dirs = []
for name in os.listdir('store'):
dirs.append(name)
return dirs
def upload(dir, chunk_size, file):
if not dir:
raise ValueError('require dir')
args = {
'dir': dir,
'chunk_size': int(chunk_size),
}
if os.path.exists(f"store/{args['dir']}"):
shutil.rmtree(f"store/{args['dir']}")
shutil.unpack_archive(file.name, f"store/{args['dir']}")
docs = docs_load(args)
if args['chunk_size'] > 0:
docs = chunk_split(docs, int(chunk_size))
vector_save(docs, args)
return f"saved store/{args['dir']}"
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