Update README.md

README.md

@@ -26,17 +26,17 @@ 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",
@@ -44,21 +44,26 @@ vectorstore = snapshot_download(repo_id="nickmuchi/CFA_Level_1_Text_Embeddings",
                                 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: "
 
@@ -80,4 +85,6 @@ search = docsearch.similarity_search(question, k=4)
 for item in search:
     print(item.page_content)
     print(f"From page: {item.metadata['page']}")
-    print("---")
+    print("---")
+
+```