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---
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("---") |