# 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("---")
```
task_categories:question-answering task_categories:summarization task_categories:conversational task_categories:sentence-similarity language:en license:apache-2.0 faiss langchain instructor embeddings vector stores books LLM region:us
{"language": ["en"], "license": "apache-2.0", "task_categories": ["question-answering", "summarization", "conversational", "sentence-similarity"], "pretty_name": "FAISS Vector Store of Embeddings for Books", "tags": ["faiss", "langchain", "instructor embeddings", "vector stores", "books", "LLM"]}
TAGS
#task_categories-question-answering #task_categories-summarization #task_categories-conversational #task_categories-sentence-similarity #language-English #license-apache-2.0 #faiss #langchain #instructor embeddings #vector stores #books #LLM #region-us 

# 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! 

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




# 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! 

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
TAGS
#task_categories-question-answering #task_categories-summarization #task_categories-conversational #task_categories-sentence-similarity #language-English #license-apache-2.0 #faiss #langchain #instructor embeddings #vector stores #books #LLM #region-us 
 # 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! 

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
passage: TAGS
#task_categories-question-answering #task_categories-summarization #task_categories-conversational #task_categories-sentence-similarity #language-English #license-apache-2.0 #faiss #langchain #instructor embeddings #vector stores #books #LLM #region-us 
# 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! 

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