license: apache-2.0
task_categories:
- question-answering
- summarization
- conversational
- sentence-similarity
language:
- en
pretty_name: >-
FAISS Vector Store of Embeddings of the Chartered Financial Analysts Level 1
Curriculum
tags:
- faiss
- langchain
- instructor embeddings
- vector stores
- LLM
Vector store of embeddings for CFA Level 1 Curriculum
This is a faiss vector store created with Sentence Transformer 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
Download data Load to use with LangChain
''' pip install -qqq langchain sentence_transformers faiss-cpu huggingface_hub import os from langchain.embeddings import HuggingFaceEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores.faiss import FAISS from huggingface_hub import snapshot_download '''
download the vectorstore for the book you want
''' cache_dir="cfa_level_1_cache" vectorstore = snapshot_download(repo_id="nickmuchi/CFA_Level_1_Text_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 = f"cfa/cfa_level_1"
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 text
embed_instruction = "Represent the financial paragraph for document retrieval: " query_instruction = "Represent the question for retrieving supporting documents: "
model_sbert = "sentence-transformers/all-mpnet-base-v2" sbert_emb = HuggingFaceEmbeddings(model_name=model_sbert)
model_instr = "hkunlp/instructor-large" instruct_emb = HuggingFaceInstructEmbeddings(model_name=model_instr, embed_instruction=embed_instruction, query_instruction=query_instruction)
load vector store to use with langchain
docsearch = FAISS.load_local(folder_path=target_path, embeddings=sbert_emb)
similarity search
question = "How do you hedge the interest rate risk of an MBS?" search = docsearch.similarity_search(question, k=4)
for item in search: print(item.page_content) print(f"From page: {item.metadata['page']}") print("---")