Proper PEP8 formatting

scripts/parser/open_ai_func.py

@@ -5,28 +5,29 @@ import tiktoken
 from langchain.vectorstores import FAISS
 from langchain.embeddings import OpenAIEmbeddings
 
-#from langchain.embeddings import HuggingFaceEmbeddings
-#from langchain.embeddings import HuggingFaceInstructEmbeddings
-#from langchain.embeddings import CohereEmbeddings
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.embeddings import HuggingFaceInstructEmbeddings
+# from langchain.embeddings import CohereEmbeddings
 
 from retry import retry
 
 
-
 def num_tokens_from_string(string: str, encoding_name: str) -> int:
-# Function to convert string to tokens and estimate user cost.
+    # Function to convert string to tokens and estimate user cost.
     encoding = tiktoken.get_encoding(encoding_name)
     num_tokens = len(encoding.encode(string))
-    total_price = ((num_tokens/1000) * 0.0004)
+    total_price = ((num_tokens / 1000) * 0.0004)
     return num_tokens, total_price
 
+
 @retry(tries=10, delay=60)
 def store_add_texts_with_retry(store, i):
     store.add_texts([i.page_content], metadatas=[i.metadata])
-    #store_pine.add_texts([i.page_content], metadatas=[i.metadata])
+    # store_pine.add_texts([i.page_content], metadatas=[i.metadata])
+
 
 def call_openai_api(docs, folder_name):
-# Function to create a vector store from the documents and save it to disk.
+    # Function to create a vector store from the documents and save it to disk.
 
     # create output folder if it doesn't exist
     if not os.path.exists(f"outputs/{folder_name}"):
@@ -37,21 +38,22 @@ def call_openai_api(docs, folder_name):
     # remove the first element from docs
     docs.pop(0)
     # cut first n docs if you want to restart
-    #docs = docs[:n]
+    # docs = docs[:n]
     c1 = 0
     # pinecone.init(
     #     api_key="",  # find at app.pinecone.io
     #     environment="us-east1-gcp"  # next to api key in console
     # )
-    #index_name = "pandas"
+    # index_name = "pandas"
     store = FAISS.from_documents(docs_test, OpenAIEmbeddings())
-    #store_pine = Pinecone.from_documents(docs_test, OpenAIEmbeddings(), index_name=index_name)
+    # store_pine = Pinecone.from_documents(docs_test, OpenAIEmbeddings(), index_name=index_name)
 
     # Uncomment for MPNet embeddings
     # model_name = "sentence-transformers/all-mpnet-base-v2"
     # hf = HuggingFaceEmbeddings(model_name=model_name)
     # store = FAISS.from_documents(docs_test, hf)
-    for i in tqdm(docs, desc="Embedding 🦖", unit="docs", total=len(docs), bar_format='{l_bar}{bar}| Time Left: {remaining}'):
+    for i in tqdm(docs, desc="Embedding 🦖", unit="docs", total=len(docs),
+                  bar_format='{l_bar}{bar}| Time Left: {remaining}'):
         try:
             store_add_texts_with_retry(store, i)
         except Exception as e:
@@ -64,20 +66,20 @@ def call_openai_api(docs, folder_name):
         c1 += 1
     store.save_local(f"outputs/{folder_name}")
 
+
 def get_user_permission(docs, folder_name):
-# Function to ask user permission to call the OpenAI api and spend their OpenAI funds.
+    # Function to ask user permission to call the OpenAI api and spend their OpenAI funds.
     # Here we convert the docs list to a string and calculate the number of OpenAI tokens the string represents.
-    #docs_content = (" ".join(docs))
+    # docs_content = (" ".join(docs))
     docs_content = ""
     for doc in docs:
         docs_content += doc.page_content
 
-
     tokens, total_price = num_tokens_from_string(string=docs_content, encoding_name="cl100k_base")
     # Here we print the number of tokens and the approx user cost with some visually appealing formatting.
     print(f"Number of Tokens = {format(tokens, ',d')}")
     print(f"Approx Cost = ${format(total_price, ',.2f')}")
-    #Here we check for user permission before calling the API.
+    # Here we check for user permission before calling the API.
     user_input = input("Price Okay? (Y/N) \n").lower()
     if user_input == "y":
         call_openai_api(docs, folder_name)