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Vector store of embeddings for books
- "1984" by George Orwell
- "The Almanac of Naval Ravikant" by Eric Jorgenson
This is a faiss vector store created with instructor embeddings using LangChain . Use it for similarity search, question answering or anything else that leverages embeddings! π
Creating these embeddings can take a while so here's a convenient, downloadable one π€
How to use
- Specify the book from one of the following:
"1984"
"The Almanac of Naval Ravikant"
- Download data
- Load to use with LangChain
pip install -qqq langchain InstructorEmbedding sentence_transformers faiss-cpu huggingface_hub
import os
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores.faiss import FAISS
from huggingface_hub import snapshot_download
# download the vectorstore for the book you want
BOOK="1984"
cache_dir=f"{book}_cache"
vectorstore = snapshot_download(repo_id="calmgoose/book-embeddings",
repo_type="dataset",
revision="main",
allow_patterns=f"books/{BOOK}/*", # to download only the one book
cache_dir=cache_dir,
)
# get path to the `vectorstore` folder that you just downloaded
# we'll look inside the `cache_dir` for the folder we want
target_dir = BOOK
# Walk through the directory tree recursively
for root, dirs, files in os.walk(cache_dir):
# Check if the target directory is in the list of directories
if target_dir in dirs:
# Get the full path of the target directory
target_path = os.path.join(root, target_dir)
# load embeddings
# this is what was used to create embeddings for the book
embeddings = HuggingFaceInstructEmbeddings(
embed_instruction="Represent the book passage for retrieval: ",
query_instruction="Represent the question for retrieving supporting texts from the book passage: "
)
# load vector store to use with langchain
docsearch = FAISS.load_local(folder_path=target_path, embeddings=embeddings)
# similarity search
question = "Who is big brother?"
search = docsearch.similarity_search(question, k=4)
for item in search:
print(item.page_content)
print(f"From page: {item.metadata['page']}")
print("---")
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