资源简介：
---
license: apache-2.0
task_categories:
- question-answering
- summarization
- conversational
- sentence-similarity
language:
- en
pretty_name: FAISS Vector Store of Embeddings for Books
tags:
- faiss
- langchain
- instructor embeddings
- vector stores
- books
- LLM
---
# Vector store of embeddings for books
- **"1984" by George Orwell**
- **"The Almanac of Naval Ravikant" by Eric Jorgenson**
This is a [faiss](https://github.com/facebookresearch/faiss) vector store created with [instructor embeddings](https://github.com/HKUNLP/instructor-embedding) using [LangChain](https://langchain.readthedocs.io/en/latest/modules/indexes/examples/embeddings.html#instructembeddings) . Use it for similarity search, question answering or anything else that leverages embeddings! 😃
Creating these embeddings can take a while so here's a convenient, downloadable one 🤗
## How to use
1. Specify the book from one of the following:
- `"1984"`
- `"The Almanac of Naval Ravikant"`
3. Download data
4. Load to use with LangChain
```
pip install -qqq langchain InstructorEmbedding sentence_transformers faiss-cpu huggingface_hub
```
```python
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("---")
```
