File size: 3,010 Bytes
94bdfd0 | 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 | # from langchain.document_loaders import DirectoryLoader
from langchain_community.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document
# from langchain.embeddings import OpenAIEmbeddings
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_community.document_loaders import TextLoader,PyPDFLoader
import openai
from dotenv import load_dotenv
import os
import shutil
from langchain_huggingface import HuggingFaceEmbeddings
# import nltk
# import sys
# sys.path.append('/home/kyzhang24/.local/lib/python3.10/site-packages')
# nltk.download('punkt')
# Load environment variables. Assumes that project contains .env file with API keys
load_dotenv()
#---- Set OpenAI API key
# Change environment variable name from "OPENAI_API_KEY" to the name given in
# your .env file.
# API_SECRET_KEY = "sk-r0WeYOdkMjzYdnSxEcC8B931Aa904e4bBaCcAc2a57D803F1"
# BASE_URL = "https://svip.xty.app/v1"
# os.environ["OPENAI_API_KEY"] = API_SECRET_KEY
# os.environ["OPENAI_API_BASE"] = BASE_URL
# openai.api_key = os.environ['OPENAI_API_KEY']
# API_SECRET_KEY = "sk-r0WeYOdkMjzYdnSxEcC8B931Aa904e4bBaCcAc2a57D803F1"
# BASE_URL = "https://svip.xty.app/v1"
# os.environ["OPENAI_API_KEY"] = API_SECRET_KEY
# os.environ["OPENAI_API_BASE"] = BASE_URL
# openai.api_key = os.environ['OPENAI_API_KEY']
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
CHROMA_PATH = "chroma"
def main():
generate_data_store()
def generate_data_store():
documents = load_documents()
chunks = split_text(documents)
save_to_chroma(chunks)
def load_documents():
# loader = TextLoader(DATA_PATH)
# documents = loader.load()
# return documents
loaders = [TextLoader('./term.txt', encoding='utf-8'),TextLoader('./corpus.txt', encoding='utf-8'),TextLoader('./law_explanation.txt', encoding='utf-8')]
docs = []
for loader in loaders:
pages = loader.load()
docs.extend(pages)
return docs
def split_text(documents: list[Document]):
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=300,
chunk_overlap=100,
length_function=len,
add_start_index=True,
)
chunks = text_splitter.split_documents(documents)
print(f"Split {len(documents)} documents into {len(chunks)} chunks.")
document = chunks[10]
print(document.page_content)
print(document.metadata)
return chunks
def save_to_chroma(chunks: list[Document]):
# Clear out the database first.
if os.path.exists(CHROMA_PATH):
shutil.rmtree(CHROMA_PATH)
# Create a new DB from the documents.
# old: OpenAIEmbeddings(model="text-embedding-3-large")
db = Chroma.from_documents(
chunks, embeddings, persist_directory=CHROMA_PATH
)
db.persist()
print(f"Saved {len(chunks)} chunks to {CHROMA_PATH}.")
if __name__ == "__main__":
main()
|